1. Model-guided therapy uses patient-specific models to complement image-guided therapy, bringing treatment closer to precise diagnosis, accurate prognosis assessment, and individualized planning and validation of therapy.
2. TIMMS is an IT system that facilitates model-guided therapy through interoperability of data, images, models, and tools to support the therapeutic intervention.
3. Patient-specific models in TIMMS must represent multidimensional and multiscale patient data, interface various system components, and link model components meaningfully while maintaining model accuracy over time.
3D printing offers benefits for healthcare such as rapid prototyping, mass customization, and ability to create complex geometries that cannot be made through other methods. However, it also faces limitations including being time consuming for all but rare cases, limited material selection, size limitations, and lack of creative uses. Areas that have seen success with 3D printing include prosthetics/exoskeletons through improved fitting, design features, aesthetics and cost reduction. It also shows promise for pre-surgical applications like planning, education, training and surgical guides as well as fields like bioprinting, tissue engineering, and medical education. For 3D printing to reach its potential in healthcare, reimbursement, regulatory approval,
Image guided surgery involves using preoperative scans like MRI or CT to create 3D reconstructions of the surgical area. This information can be used for surgical planning, simulation, and navigation during the procedure. For navigation, the 3D models are registered to the patient in the operating room using probes to locate anatomical landmarks. This allows the surgeon to view internal structures and track the position of surgical tools to aid precision. Key benefits are improved accuracy, reduced risks to vital structures, and assistance for complex cases where normal anatomy is distorted.
This document discusses image guided surgery, which uses computer technology and 3D imaging like CT and MRI scans to guide surgical interventions. Key aspects covered include:
- Image guidance allows surgeons to view a patient's anatomy during surgery to locate structures hidden from direct vision.
- Registration aligns the 3D imaging with the patient's actual anatomy using tracking devices and fiducial markers.
- Volume rendering and surface rendering techniques are used to visualize 3D models of the patient's anatomy overlaid during surgery.
- Accuracy depends on factors like registration error and tracking device precision. Image guidance is useful for locating small structures in complex areas like the skull base.
Professor Harrison Bai, Artificial Intelligence Applications in Radiology_mHe...Levi Shapiro
The document discusses several active areas of work in artificial intelligence applications in radiology at Brown University's AI radiology lab, including COVID-19 detection from chest x-rays, tumor assessment, stroke diagnosis, and more. It provides details on techniques like contrast dropout to deal with missing data, human-in-the-loop approaches, automatic quality estimation, treatment response evaluation, and federated learning to share models without sharing patient data. Performance results and example visualizations from various models are also included.
Comparative Study on Cancer Images using Watershed Transformationijtsrd
Digital images are exceptionally huge in the medical image diagnosis frameworks. Image analysis and segmentation are very important tasks in the medical image processing particularly in the field of CAD systems. Visual inspection requires being clear in diagnosis process where the correct region which is affected, need to be separated. Medical imaging plays a very crucial role in all stages of the medical decision process. There are various medical imaging modalities in which mammography are used to detect breast cancer where as MRI for brain tumor and CT for lung cancer. The objective of this paper is to compare the cancer images with different modalities using watershed transformation using metrics. M. Najela Fathin | Dr. S. Shajun Nisha | Dr. M. Mohamed Sathik"Comparative Study on Cancer Images using Watershed Transformation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12767.pdf http://www.ijtsrd.com/computer-science/other/12767/comparative-study-on-cancer-images-using-watershed-transformation/m-najela-fathin
This document summarizes current medical image processing research being conducted at various universities. It describes projects involving ECG compression, MRI using blood oxygen level detection, spinal image fusion to improve diagnosis, segmenting anatomical structures from MRI, and using elasticity imaging to detect kidney transplant rejection. It also lists programs for processing MRI data and retinex image processing, as well as websites with medical image test data and news about diagnostic imaging.
3D printing offers benefits for healthcare such as rapid prototyping, mass customization, and ability to create complex geometries that cannot be made through other methods. However, it also faces limitations including being time consuming for all but rare cases, limited material selection, size limitations, and lack of creative uses. Areas that have seen success with 3D printing include prosthetics/exoskeletons through improved fitting, design features, aesthetics and cost reduction. It also shows promise for pre-surgical applications like planning, education, training and surgical guides as well as fields like bioprinting, tissue engineering, and medical education. For 3D printing to reach its potential in healthcare, reimbursement, regulatory approval,
Image guided surgery involves using preoperative scans like MRI or CT to create 3D reconstructions of the surgical area. This information can be used for surgical planning, simulation, and navigation during the procedure. For navigation, the 3D models are registered to the patient in the operating room using probes to locate anatomical landmarks. This allows the surgeon to view internal structures and track the position of surgical tools to aid precision. Key benefits are improved accuracy, reduced risks to vital structures, and assistance for complex cases where normal anatomy is distorted.
This document discusses image guided surgery, which uses computer technology and 3D imaging like CT and MRI scans to guide surgical interventions. Key aspects covered include:
- Image guidance allows surgeons to view a patient's anatomy during surgery to locate structures hidden from direct vision.
- Registration aligns the 3D imaging with the patient's actual anatomy using tracking devices and fiducial markers.
- Volume rendering and surface rendering techniques are used to visualize 3D models of the patient's anatomy overlaid during surgery.
- Accuracy depends on factors like registration error and tracking device precision. Image guidance is useful for locating small structures in complex areas like the skull base.
Professor Harrison Bai, Artificial Intelligence Applications in Radiology_mHe...Levi Shapiro
The document discusses several active areas of work in artificial intelligence applications in radiology at Brown University's AI radiology lab, including COVID-19 detection from chest x-rays, tumor assessment, stroke diagnosis, and more. It provides details on techniques like contrast dropout to deal with missing data, human-in-the-loop approaches, automatic quality estimation, treatment response evaluation, and federated learning to share models without sharing patient data. Performance results and example visualizations from various models are also included.
Comparative Study on Cancer Images using Watershed Transformationijtsrd
Digital images are exceptionally huge in the medical image diagnosis frameworks. Image analysis and segmentation are very important tasks in the medical image processing particularly in the field of CAD systems. Visual inspection requires being clear in diagnosis process where the correct region which is affected, need to be separated. Medical imaging plays a very crucial role in all stages of the medical decision process. There are various medical imaging modalities in which mammography are used to detect breast cancer where as MRI for brain tumor and CT for lung cancer. The objective of this paper is to compare the cancer images with different modalities using watershed transformation using metrics. M. Najela Fathin | Dr. S. Shajun Nisha | Dr. M. Mohamed Sathik"Comparative Study on Cancer Images using Watershed Transformation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12767.pdf http://www.ijtsrd.com/computer-science/other/12767/comparative-study-on-cancer-images-using-watershed-transformation/m-najela-fathin
This document summarizes current medical image processing research being conducted at various universities. It describes projects involving ECG compression, MRI using blood oxygen level detection, spinal image fusion to improve diagnosis, segmenting anatomical structures from MRI, and using elasticity imaging to detect kidney transplant rejection. It also lists programs for processing MRI data and retinex image processing, as well as websites with medical image test data and news about diagnostic imaging.
Lumbar disk 3D modeling from limited number of MRI axial slices IJECEIAES
This paper studies the problem of clinical MRI analysis in the field of lumbar intervertebral disk herniation diagnosis. It discusses the possibility of assisting radiologists in reading the patient's MRI images by constructing a 3D model for the region of interest using simple computer vision methods. We use axial MRI slices of the lumbar area. The proposed framework works with a very small number of MRI slices and goes through three main stages. Namely, the region of interest extraction and enhancement, inter-slice interpolation, and 3D model construction. We use the Marching Cubes algorithm to construct the 3D model of the region of interest. The validation of our 3D models is based on a radiologist's analysis of the models. We tested the proposed 3D model construction on 83 cases and We have a 95% accuracy according to the radiologist evaluation. This study shows that 3D model construction can greatly ease the task of the radiologist which enhances the working experience. This leads eventually to a more accurate and easy diagnosis process.
Track 6. Technological innovations in biomedical training and practice
Authors: Jesús M Gonçalves, M J Sanchez-Ledesma, P Ruisoto, M Jaramillo, J J Jimenez and J A Juanes
Today, computer aided system is widely used in various fields. Among them, the brain tumor detection is an important task in medical image processing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of brain tumors for cancer diagnosis, from large amount of Magnetic Resonance Imaging MRI images generated in clinical routine, is a difficult and time consuming task or even generates errors. So, the automatic brain tumor segmentation is needed to segment tumor. The purpose of the thesis is to detect the brain tumor quickly and accurately from the MRI brain image. In the system, the average filter is used to remove noise and make smooth an input MRI image and threshold segmentation is applied to segment tumor region from MRI brain images. Region properties method is used to detect the tumor region exactly. And then, the equation of the tumor region in the system is effectively applied in any shape of the tumor region. Moe Moe Aye | Kyaw Kyaw Lin "Brain Tumor Detection System for MRI Image" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27864.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/27864/brain-tumor-detection-system-for-mri-image/moe-moe-aye
DETECTING BRAIN TUMOUR FROM MRI IMAGE USING MATLAB GUI PROGRAMMEIJCSES Journal
Engineers have been actively developing tools to detect tumors and to process medical images. Medical image segmentation is a powerful tool that is often used to detect tumors. Many scientists and researchers are working to develop and add more features to this tool. This project is about detecting Brain tumors from MRI images using an interface of GUI in Matlab. Using the GUI, this program can use various combinations of segmentation, filters, and other image processing algorithms to achieve the best results.
We start with filtering the image using Prewitt horizontal edge-emphasizing filter. The next step for detecting tumor is "watershed pixels." The most important part of this project is that all the Matlab programs work with GUI “Matlab guide”. This allows us to use various combinations of filters, and other
image processing techniques to arrive at the best result that can help us detect brain tumors in their early stages.
ANALYSIS OF WATERMARKING TECHNIQUES FOR MEDICAL IMAGES PRESERVING ROI cscpconf
The document discusses watermarking techniques for medical images to preserve the region of interest (ROI) during transmission. It first provides background on the need for security in sharing medical images over networks. It then summarizes various techniques for segmenting the ROI from medical images, including thresholding, clustering, and edge detection methods applied to MRI and CT scans. The goal of the watermarking is to apply marks only to the region outside the ROI (RONI) to authenticate images without affecting diagnosis.
Brain tumor detection and localization in magnetic resonance imagingijitcs
A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the
surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and
responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate
in the world. Great knowledge and experience on radiology are required for accurate tumor detection in
medical imaging. Automation of tumor detection is required because there might be a shortage of skilled
radiologists at a time of great need. We propose an automatic brain tumor detectionand localization
framework that can detect and localize brain tumor in magnetic resonance imaging. The proposed brain
tumor detection and localization framework comprises five steps: image acquisition, pre-processing, edge
detection, modified histogram clustering and morphological operations. After morphological operations,
tumors appear as pure white color on pure black backgrounds. We used 50 neuroimages to optimize our
system and 100 out-of-sample neuroimages to test our system. The proposed tumor detection and localization
system was found to be able to accurately detect and localize brain tumor in magnetic resonance imaging.
The preliminary results demonstrate how a simple machine learning classifier with a set of simple
image-based features can result in high classification accuracy. The preliminary results also demonstrate the
efficacy and efficiency of our five-step brain tumor detection and localization approach and motivate us to
extend this framework to detect and localize a variety of other types of tumors in other types of medical
imagery.
This document discusses medical image processing and its application to breast cancer detection. It provides an overview of digital image processing techniques used in medical imaging like X-rays, mammography, ultrasound, MRI and CT. Computer-aided diagnosis (CAD) helps in tasks like visualization, detection, localization, segmentation and classification of medical images. For breast cancer detection specifically, the document discusses mammography and challenges in detecting tumors in dense breast tissue. It also reviews several published methods for segmenting and analyzing lesions in mammograms and evaluates their performance based on parameters like true positives, false positives, etc.
Image registraion is vital component in modern radiotherpay. Accuracy is important as output of image registraion process is input of another process in radiation therapy
IRJET - Deep Learning based Bone Tumor Detection with Real Time DatasetsIRJET Journal
This document presents a proposed method for detecting bone tumors using deep learning and recurrent neural networks. Specifically, it involves using MRI images as input data and extracting features through segmentation and techniques like HOG. Recurrent neural networks like simple RNNs and LSTMs are then used to both impute any missing data in images and predict bone tumors. This approach is meant to increase accuracy over other methods by handling missing image parts. The proposed system is analyzed to show it can provide accurate bone tumor detection and diagnostic suggestions when evaluating medical examination data.
Intra Report- St. James' Hospital Medical Physics Muhammad Alli
The MPBE department (Medical Physics and Bioengineering) provides technical services to the hospital by taking care of medical equipment, the calibration of imaging equipment as well as services to ensuring the safe operation of equipment. The medical physicists also provide services in nuclear medicine. Radioiodine therapy is a service the hospital provides, one of my major goals setting out to work at St. James’s was to learn about radiotherapy, I had a role looking though research papers to try and find information which could help with the way the radioiodine therapy the hospital provides is given, that role was elegantly supported with other relevant work, such as contamination monitoring and experimental work which built an amazing knowledgebase for me. I took part in the NIMIS project and delivered a presentation on a new piece of dose tracking software to the MPBE department.
I carried out many other short term roles which served to develop me in many areas within and including science, IT and engineering as well as developing my people skills. I learned how to interact on a technical level with an interdisciplinary team. As well as gain an understanding of team dynamics, organizational and project management. The experience was very enriching all-around and I would gladly recommend it to future students as an INTRA placement.
Computer aid in medical instrument term paper PPTKoushik Sarkar
The document discusses various computer-aided medical instruments and technologies. It describes several existing computerized instruments such as X-ray machines, CT scanners, MRI machines, and ECG machines. It also discusses challenges with existing instruments and ongoing research into 3D graphical interfaces for computer-assisted surgery, computer-aided surgery using robotics, direct brain interfaces between humans, and medical apps for Android mobile devices. The document emphasizes how computers and medical technology can help improve diagnosis, aid surgery planning and procedures, and enhance information access for healthcare providers.
Stereotaxy uses 3D imaging to guide surgical procedures. It began with frames attached to patients' heads but now uses frameless techniques. Frameless stereotaxy uses preoperative imaging, a tracked probe, and registration of images to guide surgery. Sources of error include imaging distortions and brain shift during surgery. Neuronavigation aids in precisely locating tumors and critical structures during brain surgery. Developments aim to improve accuracy, ease of use, and expanded applications.
Medical image processing involves acquiring medical images through modalities like X-rays, CT, MRI, using techniques like ultrasound. The images are then preprocessed, segmented, analyzed and classified to diagnose diseases or detect abnormalities. Key applications include tumor detection, monitoring bone strength, and medical image fusion to enable accurate analysis and remote sharing of data to enhance diagnosis and treatment.
Iaetsd classification of lung tumour usingIaetsd Iaetsd
This document describes a study that aims to classify lung tumors using geometric and texture features extracted from chest x-ray images. The study uses 75 chest x-ray images (25 from small-cell lung cancer, 25 from non-small cell lung cancer, and 25 from tuberculosis) to extract geometric features like area, shape, and distance from texture features calculated using gray level co-occurrence matrices. Active shape models are used to segment the lung fields for feature extraction. The extracted features are then analyzed to determine the optimal features for classifying different types of lung abnormalities.
Machine Learning for Medical Image Analysis:What, where and how?Debdoot Sheet
A great career advice for EECS (Electrical, electronics and computer science) graduates interested in machine vision and some advice for a PhD career in Medical Image Analysis.
Neutrosophic sets and fuzzy c means clustering for improving ct liver image s...Aboul Ella Hassanien
The document proposes a hybrid method using neutrosophic sets and fuzzy c-means clustering to improve liver segmentation in CT images. It transforms the image into neutrosophic domains of truth, indeterminacy, and falsity. Thresholds are adapted using fuzzy c-means to binarize the domains. Experimental results on 30 abdominal CT images found 88% accuracy by Jaccard index and 94% by Dice coefficient, outperforming other methods. The approach effectively handles noise and uncertainty to produce clear liver boundaries.
This document summarizes IGRT techniques for prostate cancer radiation therapy. It discusses the history of using radiation to treat prostate cancer dating back to 1909. It describes advances like 3D conformal radiation therapy and IMRT which allow shaping radiation doses to the target volume. The document outlines the simulation, planning, and contouring process including using fiducial markers and CT/MRI imaging. It discusses dose escalation trials and techniques to reduce organ motion like immobilization devices. Interfractional and intrafractional prostate motion is analyzed from several studies.
Medical image fusion combines information from different imaging modalities like PET, CT, MRI into a single image. It has revolutionized medical diagnosis in various areas like oncology, brain imaging, and cardiology. Hybrid imaging using external markers or software registration are common fusion approaches. PET/CT fusion provides improved anatomical localization and cancer staging by combining metabolic PET information with anatomical CT data. New applications of fusion include image-guided interventions using ultrasound and CT fusion.
This document discusses the importance of treatment verification in radiotherapy and outlines the process. It notes that even small errors can have negative consequences so treatment verification is essential to ensure the right dose is delivered to the right area. The key aspects of treatment verification are machine setup, monitor units, patient positioning and imaging by comparing images to references. Errors can be systematic from planning or random from daily variations; various methods are described to reduce errors and ensure treatments are accurately delivered.
The Role of Computers in Medical PhysicsVictor Ekpo
The document discusses the various roles of computers in medical physics. It describes how computers are used for tasks like data conversion, database management, image display, processing and analysis. Computers aid in areas such as radiodiagnosis, radiotherapy treatment planning, dosimetry and various medical imaging modalities. They provide benefits like speed, automation, accuracy and ability to store and share large amounts of data. Overall, the integration of computers has greatly enhanced the field of medical physics.
Surgical Process Modeling: Theory, Methods, and Applicationstneumuth
This document presents Thomas Neumuth's habilitation thesis on surgical process modeling. The thesis aims to develop innovative methods for modeling surgical processes to address limitations of existing top-down modeling approaches. These include subjective bias, high costs and time requirements, and inability to account for variability. The objectives are to develop ICT methods for describing, acquiring, abstracting and utilizing surgical process models, and evaluate them in clinical applications. The thesis covers developing an ontology for surgical processes, designing similarity metrics, data acquisition strategies, generalizing models and generating workflow schemata, and clinical applications in neurosurgery, pediatric surgery and ophthalmology. The overall goal is to provide a systematic approach to surgical process modeling and evaluation to improve documentation
Challenges and opportunities for machine learning in biomedical researchFranciscoJAzuajeG
1. Machine learning faces challenges in biomedical research due to data heterogeneity, lack of labeled data, and complexity in biological patterns and networks.
2. Combining machine learning and biological network models can help address these challenges by encoding data in biologically meaningful networks and extracting network-based features for prediction.
3. Examples applying this approach to cancer datasets showed that models based on network centrality features outperformed other methods, and deep learning using these features achieved the best prediction performance across multiple neuroblastoma datasets.
Lumbar disk 3D modeling from limited number of MRI axial slices IJECEIAES
This paper studies the problem of clinical MRI analysis in the field of lumbar intervertebral disk herniation diagnosis. It discusses the possibility of assisting radiologists in reading the patient's MRI images by constructing a 3D model for the region of interest using simple computer vision methods. We use axial MRI slices of the lumbar area. The proposed framework works with a very small number of MRI slices and goes through three main stages. Namely, the region of interest extraction and enhancement, inter-slice interpolation, and 3D model construction. We use the Marching Cubes algorithm to construct the 3D model of the region of interest. The validation of our 3D models is based on a radiologist's analysis of the models. We tested the proposed 3D model construction on 83 cases and We have a 95% accuracy according to the radiologist evaluation. This study shows that 3D model construction can greatly ease the task of the radiologist which enhances the working experience. This leads eventually to a more accurate and easy diagnosis process.
Track 6. Technological innovations in biomedical training and practice
Authors: Jesús M Gonçalves, M J Sanchez-Ledesma, P Ruisoto, M Jaramillo, J J Jimenez and J A Juanes
Today, computer aided system is widely used in various fields. Among them, the brain tumor detection is an important task in medical image processing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of brain tumors for cancer diagnosis, from large amount of Magnetic Resonance Imaging MRI images generated in clinical routine, is a difficult and time consuming task or even generates errors. So, the automatic brain tumor segmentation is needed to segment tumor. The purpose of the thesis is to detect the brain tumor quickly and accurately from the MRI brain image. In the system, the average filter is used to remove noise and make smooth an input MRI image and threshold segmentation is applied to segment tumor region from MRI brain images. Region properties method is used to detect the tumor region exactly. And then, the equation of the tumor region in the system is effectively applied in any shape of the tumor region. Moe Moe Aye | Kyaw Kyaw Lin "Brain Tumor Detection System for MRI Image" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27864.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/27864/brain-tumor-detection-system-for-mri-image/moe-moe-aye
DETECTING BRAIN TUMOUR FROM MRI IMAGE USING MATLAB GUI PROGRAMMEIJCSES Journal
Engineers have been actively developing tools to detect tumors and to process medical images. Medical image segmentation is a powerful tool that is often used to detect tumors. Many scientists and researchers are working to develop and add more features to this tool. This project is about detecting Brain tumors from MRI images using an interface of GUI in Matlab. Using the GUI, this program can use various combinations of segmentation, filters, and other image processing algorithms to achieve the best results.
We start with filtering the image using Prewitt horizontal edge-emphasizing filter. The next step for detecting tumor is "watershed pixels." The most important part of this project is that all the Matlab programs work with GUI “Matlab guide”. This allows us to use various combinations of filters, and other
image processing techniques to arrive at the best result that can help us detect brain tumors in their early stages.
ANALYSIS OF WATERMARKING TECHNIQUES FOR MEDICAL IMAGES PRESERVING ROI cscpconf
The document discusses watermarking techniques for medical images to preserve the region of interest (ROI) during transmission. It first provides background on the need for security in sharing medical images over networks. It then summarizes various techniques for segmenting the ROI from medical images, including thresholding, clustering, and edge detection methods applied to MRI and CT scans. The goal of the watermarking is to apply marks only to the region outside the ROI (RONI) to authenticate images without affecting diagnosis.
Brain tumor detection and localization in magnetic resonance imagingijitcs
A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the
surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and
responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate
in the world. Great knowledge and experience on radiology are required for accurate tumor detection in
medical imaging. Automation of tumor detection is required because there might be a shortage of skilled
radiologists at a time of great need. We propose an automatic brain tumor detectionand localization
framework that can detect and localize brain tumor in magnetic resonance imaging. The proposed brain
tumor detection and localization framework comprises five steps: image acquisition, pre-processing, edge
detection, modified histogram clustering and morphological operations. After morphological operations,
tumors appear as pure white color on pure black backgrounds. We used 50 neuroimages to optimize our
system and 100 out-of-sample neuroimages to test our system. The proposed tumor detection and localization
system was found to be able to accurately detect and localize brain tumor in magnetic resonance imaging.
The preliminary results demonstrate how a simple machine learning classifier with a set of simple
image-based features can result in high classification accuracy. The preliminary results also demonstrate the
efficacy and efficiency of our five-step brain tumor detection and localization approach and motivate us to
extend this framework to detect and localize a variety of other types of tumors in other types of medical
imagery.
This document discusses medical image processing and its application to breast cancer detection. It provides an overview of digital image processing techniques used in medical imaging like X-rays, mammography, ultrasound, MRI and CT. Computer-aided diagnosis (CAD) helps in tasks like visualization, detection, localization, segmentation and classification of medical images. For breast cancer detection specifically, the document discusses mammography and challenges in detecting tumors in dense breast tissue. It also reviews several published methods for segmenting and analyzing lesions in mammograms and evaluates their performance based on parameters like true positives, false positives, etc.
Image registraion is vital component in modern radiotherpay. Accuracy is important as output of image registraion process is input of another process in radiation therapy
IRJET - Deep Learning based Bone Tumor Detection with Real Time DatasetsIRJET Journal
This document presents a proposed method for detecting bone tumors using deep learning and recurrent neural networks. Specifically, it involves using MRI images as input data and extracting features through segmentation and techniques like HOG. Recurrent neural networks like simple RNNs and LSTMs are then used to both impute any missing data in images and predict bone tumors. This approach is meant to increase accuracy over other methods by handling missing image parts. The proposed system is analyzed to show it can provide accurate bone tumor detection and diagnostic suggestions when evaluating medical examination data.
Intra Report- St. James' Hospital Medical Physics Muhammad Alli
The MPBE department (Medical Physics and Bioengineering) provides technical services to the hospital by taking care of medical equipment, the calibration of imaging equipment as well as services to ensuring the safe operation of equipment. The medical physicists also provide services in nuclear medicine. Radioiodine therapy is a service the hospital provides, one of my major goals setting out to work at St. James’s was to learn about radiotherapy, I had a role looking though research papers to try and find information which could help with the way the radioiodine therapy the hospital provides is given, that role was elegantly supported with other relevant work, such as contamination monitoring and experimental work which built an amazing knowledgebase for me. I took part in the NIMIS project and delivered a presentation on a new piece of dose tracking software to the MPBE department.
I carried out many other short term roles which served to develop me in many areas within and including science, IT and engineering as well as developing my people skills. I learned how to interact on a technical level with an interdisciplinary team. As well as gain an understanding of team dynamics, organizational and project management. The experience was very enriching all-around and I would gladly recommend it to future students as an INTRA placement.
Computer aid in medical instrument term paper PPTKoushik Sarkar
The document discusses various computer-aided medical instruments and technologies. It describes several existing computerized instruments such as X-ray machines, CT scanners, MRI machines, and ECG machines. It also discusses challenges with existing instruments and ongoing research into 3D graphical interfaces for computer-assisted surgery, computer-aided surgery using robotics, direct brain interfaces between humans, and medical apps for Android mobile devices. The document emphasizes how computers and medical technology can help improve diagnosis, aid surgery planning and procedures, and enhance information access for healthcare providers.
Stereotaxy uses 3D imaging to guide surgical procedures. It began with frames attached to patients' heads but now uses frameless techniques. Frameless stereotaxy uses preoperative imaging, a tracked probe, and registration of images to guide surgery. Sources of error include imaging distortions and brain shift during surgery. Neuronavigation aids in precisely locating tumors and critical structures during brain surgery. Developments aim to improve accuracy, ease of use, and expanded applications.
Medical image processing involves acquiring medical images through modalities like X-rays, CT, MRI, using techniques like ultrasound. The images are then preprocessed, segmented, analyzed and classified to diagnose diseases or detect abnormalities. Key applications include tumor detection, monitoring bone strength, and medical image fusion to enable accurate analysis and remote sharing of data to enhance diagnosis and treatment.
Iaetsd classification of lung tumour usingIaetsd Iaetsd
This document describes a study that aims to classify lung tumors using geometric and texture features extracted from chest x-ray images. The study uses 75 chest x-ray images (25 from small-cell lung cancer, 25 from non-small cell lung cancer, and 25 from tuberculosis) to extract geometric features like area, shape, and distance from texture features calculated using gray level co-occurrence matrices. Active shape models are used to segment the lung fields for feature extraction. The extracted features are then analyzed to determine the optimal features for classifying different types of lung abnormalities.
Machine Learning for Medical Image Analysis:What, where and how?Debdoot Sheet
A great career advice for EECS (Electrical, electronics and computer science) graduates interested in machine vision and some advice for a PhD career in Medical Image Analysis.
Neutrosophic sets and fuzzy c means clustering for improving ct liver image s...Aboul Ella Hassanien
The document proposes a hybrid method using neutrosophic sets and fuzzy c-means clustering to improve liver segmentation in CT images. It transforms the image into neutrosophic domains of truth, indeterminacy, and falsity. Thresholds are adapted using fuzzy c-means to binarize the domains. Experimental results on 30 abdominal CT images found 88% accuracy by Jaccard index and 94% by Dice coefficient, outperforming other methods. The approach effectively handles noise and uncertainty to produce clear liver boundaries.
This document summarizes IGRT techniques for prostate cancer radiation therapy. It discusses the history of using radiation to treat prostate cancer dating back to 1909. It describes advances like 3D conformal radiation therapy and IMRT which allow shaping radiation doses to the target volume. The document outlines the simulation, planning, and contouring process including using fiducial markers and CT/MRI imaging. It discusses dose escalation trials and techniques to reduce organ motion like immobilization devices. Interfractional and intrafractional prostate motion is analyzed from several studies.
Medical image fusion combines information from different imaging modalities like PET, CT, MRI into a single image. It has revolutionized medical diagnosis in various areas like oncology, brain imaging, and cardiology. Hybrid imaging using external markers or software registration are common fusion approaches. PET/CT fusion provides improved anatomical localization and cancer staging by combining metabolic PET information with anatomical CT data. New applications of fusion include image-guided interventions using ultrasound and CT fusion.
This document discusses the importance of treatment verification in radiotherapy and outlines the process. It notes that even small errors can have negative consequences so treatment verification is essential to ensure the right dose is delivered to the right area. The key aspects of treatment verification are machine setup, monitor units, patient positioning and imaging by comparing images to references. Errors can be systematic from planning or random from daily variations; various methods are described to reduce errors and ensure treatments are accurately delivered.
The Role of Computers in Medical PhysicsVictor Ekpo
The document discusses the various roles of computers in medical physics. It describes how computers are used for tasks like data conversion, database management, image display, processing and analysis. Computers aid in areas such as radiodiagnosis, radiotherapy treatment planning, dosimetry and various medical imaging modalities. They provide benefits like speed, automation, accuracy and ability to store and share large amounts of data. Overall, the integration of computers has greatly enhanced the field of medical physics.
Surgical Process Modeling: Theory, Methods, and Applicationstneumuth
This document presents Thomas Neumuth's habilitation thesis on surgical process modeling. The thesis aims to develop innovative methods for modeling surgical processes to address limitations of existing top-down modeling approaches. These include subjective bias, high costs and time requirements, and inability to account for variability. The objectives are to develop ICT methods for describing, acquiring, abstracting and utilizing surgical process models, and evaluate them in clinical applications. The thesis covers developing an ontology for surgical processes, designing similarity metrics, data acquisition strategies, generalizing models and generating workflow schemata, and clinical applications in neurosurgery, pediatric surgery and ophthalmology. The overall goal is to provide a systematic approach to surgical process modeling and evaluation to improve documentation
Challenges and opportunities for machine learning in biomedical researchFranciscoJAzuajeG
1. Machine learning faces challenges in biomedical research due to data heterogeneity, lack of labeled data, and complexity in biological patterns and networks.
2. Combining machine learning and biological network models can help address these challenges by encoding data in biologically meaningful networks and extracting network-based features for prediction.
3. Examples applying this approach to cancer datasets showed that models based on network centrality features outperformed other methods, and deep learning using these features achieved the best prediction performance across multiple neuroblastoma datasets.
Paper Annotated: SinGAN-Seg: Synthetic Training Data Generation for Medical I...Devansh16
YouTube video: https://www.youtube.com/watch?v=Ao-19L0sLOI
SinGAN-Seg: Synthetic Training Data Generation for Medical Image Segmentation
Vajira Thambawita, Pegah Salehi, Sajad Amouei Sheshkal, Steven A. Hicks, Hugo L.Hammer, Sravanthi Parasa, Thomas de Lange, Pål Halvorsen, Michael A. Riegler
Processing medical data to find abnormalities is a time-consuming and costly task, requiring tremendous efforts from medical experts. Therefore, Ai has become a popular tool for the automatic processing of medical data, acting as a supportive tool for doctors. AI tools highly depend on data for training the models. However, there are several constraints to access to large amounts of medical data to train machine learning algorithms in the medical domain, e.g., due to privacy concerns and the costly, time-consuming medical data annotation process. To address this, in this paper we present a novel synthetic data generation pipeline called SinGAN-Seg to produce synthetic medical data with the corresponding annotated ground truth masks. We show that these synthetic data generation pipelines can be used as an alternative to bypass privacy concerns and as an alternative way to produce artificial segmentation datasets with corresponding ground truth masks to avoid the tedious medical data annotation process. As a proof of concept, we used an open polyp segmentation dataset. By training UNet++ using both the real polyp segmentation dataset and the corresponding synthetic dataset generated from the SinGAN-Seg pipeline, we show that the synthetic data can achieve a very close performance to the real data when the real segmentation datasets are large enough. In addition, we show that synthetic data generated from the SinGAN-Seg pipeline improving the performance of segmentation algorithms when the training dataset is very small. Since our SinGAN-Seg pipeline is applicable for any medical dataset, this pipeline can be used with any other segmentation datasets.
Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2107.00471 [eess.IV]
(or arXiv:2107.00471v1 [eess.IV] for this version)
Reach out to me:
Check out my other articles on Medium. : https://machine-learning-made-simple....
My YouTube: https://rb.gy/88iwdd
Reach out to me on LinkedIn: https://www.linkedin.com/in/devansh-d...
My Instagram: https://rb.gy/gmvuy9
My Twitter: https://twitter.com/Machine01776819
My Substack: https://devanshacc.substack.com/
Live conversations at twitch here: https://rb.gy/zlhk9y
Get a free stock on Robinhood: https://join.robinhood.com/fnud75
PO WER - XX LO Gdańsk - Mathematics and treatment of cancerAgnieszka J.
Mathematics and statistical modeling can provide benefits for cancer treatment. Mathematical models of cancer processes can help researchers better understand the underlying biological mechanisms. Models can also be used to simulate disease progression and optimize therapeutic procedures. For example, models of cancer invasion describe processes like tumor cell migration, growth, and degradation of tissue. Additionally, 3D mathematical models present a three-dimensional image of the genome and allow simulations to be performed, providing insights into cancer development. Overall, the synergy between mathematics and biology can lead to more accurate applications and improved cancer therapies.
An Introduction To Artificial Intelligence And Its Applications In Biomedical...Jill Brown
1) The document discusses the use of artificial intelligence techniques in biomedical engineering and medicine. It focuses on using AI to analyze medical signals and images to assist clinicians.
2) Key applications discussed include using neural networks and expert knowledge as intelligent agents to aid diagnosis, as well as using AI systems to automatically realign MRI images and identify corresponding slices from different scans.
3) The document also outlines the major components of AI, including problem solving, knowledge representation, and perception, and how AI can be applied through intelligent agents, a task manager, and a communication system to integrate different types of medical data and decision-making approaches.
3D Segmentation of Brain Tumor ImagingIJAEMSJORNAL
A brain tumor is a collection of anomalous cells that grow in or around the brain. Brain tumors affect the humans badly, it can disrupt proper brain function and be life-threatening. In this project, we have proposed a system to detect, segment, and classify the tumors present in the brain. Once the brain tumor is identified at the very beginning, proper treatments can be done and it may be cured.
This document discusses using management engineering principles to analyze healthcare delivery systems. It provides an example analysis of a hospital system modeled as interdependent subsystems, including the emergency department, intensive care unit, operating rooms, and nursing units. Simulation of the mathematical model revealed important relationships between the subsystems that could inform management decisions. The conclusion advocates using objective data analysis and simulation rather than subjective opinions alone for healthcare management decisions.
BRAIN TUMOR MRIIMAGE CLASSIFICATION WITH FEATURE SELECTION AND EXTRACTION USI...ijistjournal
Feature extraction is a method of capturing visual content of an image. The feature extraction is the process to represent raw image in its reduced form to facilitate decision making such as pattern classification. We have tried to address the problem of classification MRI brain images by creating a robust and more accurate classifier which can act as an expert assistant to medical practitioners. The objective of this paper is to present a novel method of feature selection and extraction. This approach combines the Intensity, Texture, shape based features and classifies the tumor as white matter, Gray matter, CSF, abnormal and normal area. The experiment is performed on 140 tumor contained brain MR images from the Internet Brain Segmentation Repository. The proposed technique has been carried out over a larger database as compare to any previous work and is more robust and effective. PCA and Linear Discriminant Analysis (LDA) were applied on the training sets. The Support Vector Machine (SVM) classifier served as a comparison of nonlinear techniques Vs linear ones. PCA and LDA methods are used to reduce the number of features used. The feature selection using the proposed technique is more beneficial as it analyses the data according to grouping class variable and gives reduced feature set with high classification accuracy.
BRAIN TUMOR MRIIMAGE CLASSIFICATION WITH FEATURE SELECTION AND EXTRACTION USI...ijistjournal
This document presents a novel approach for brain tumor classification in MRI images using feature selection and extraction. It extracts intensity, texture, and shape-based features from MRI images and applies principal component analysis (PCA) and linear discriminant analysis (LDA) for dimensionality reduction. Support vector machines (SVM) are then used to classify tumors as white matter, gray matter, CSF, abnormal or normal tissue. The technique is tested on 140 brain MRI images and achieves high classification accuracy compared to previous methods.
Digital Imaging and Communications in Medicine (DICOM) is a standard for communicating and managing medical imaging data. It defines a file format and network protocol for transmitting images and associated patient information between systems. DICOM allows different medical devices to store, transmit and display diagnostically accurate medical images, facilitating data sharing across systems.
Talk entitled "from the Virtual Human to a Digital Me" presented at the Virtual Physiological Human 2012 Conference held at IET Savoy, Savoy Place, London, 18-20 September 2012.
Theory and Practice of Integrating Machine Learning and Conventional Statisti...University of Malaya
The practice of medical decision making is changing rapidly with the development of innovative
computing technologies. The growing interest of data analysis in line with the advancement in data
science raises the question of whether machine learning can be integrated with conventional statistics
in health research. To help address this knowledge gap, this talk focuses on the conceptual
integration between conventional statistics and machine learning, with a direction towards health
research. The similarities and differences between the two are compared using mathematical
concepts and algorithms. The comparison between conventional statistics and machine learning
methods indicates that conventional statistics are the fundamental basis of machine learning, where
the black box algorithms are derived from basic mathematics, but are advanced in terms of
automated analysis, handling big data and providing interactive visualizations. While the nature of
both these methods are different, they are conceptually similar. The evidence produced here
concludes that conventional statistics and machine learning are best to be integrated to develop
automated data analysis tools. Health researchers may explore machine learning as a potential tool to
enhance conventional statistics in data analytics for added reliable validation measures.
GET IEEE BIG DATA,JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
Review of Image Watermarking Technique for MediIJARIIT
In this article, we focus on the complementary role of watermarking with respect to medical information security (integrity, authenticity …) and management. We review sample cases where watermarking has been deployed. We conclude that watermarking has found a niche role in healthcare systems, as an instrument for protection of medical information, for secure sharing and handling of medical images. The concern of medical experts on the preservation of documents diagnostic integrity remains paramount. Medical image watermarking is an appropriate method used for enhancing security and authentication of medical data, which is crucial and used for further diagnosis and reference. This paper discusses the available medical image watermarking methods for protecting and authenticating medical data. The paper focuses on algorithms for application of watermarking technique on Region of Non Interest (RONI) of the medical image preserving Region of Interest (ROI).
GRADE CATEGORIZATION OF TUMOUR CELLS WITH STANDARD AND REFERENTIAL FRONTIER A...pharmaindexing
This document summarizes a research paper that proposes a new method for classifying brain tumor grades using image processing techniques. The method involves preprocessing MRI images to isolate the tumor region using thresholding and image subtraction. The tumor area is then segmented into four quadrants. Standard points mark the initial tumor location, while growth points registered in later images indicate tumor expansion over time. Comparing growth point changes across patient images at different stages allows calculating the tumor growth rate, aiding pathologists in diagnosis and treatment recommendations.
Computer Aided System for Detection and Classification of Breast CancerIJITCA Journal
Breast cancer is one of the most important causes of death among all type of cancers for grown-up and
older women, mainly in developed countries, and its rate is rising. Since the cause of this disease is not yet
known, early detection is the best way to decrease the breast cancer mortality. At present, early detection of
breast cancer is attained by means of mammography. An intelligent computer-aided diagnosis system can
be very helpful for radiologist in detecting and diagnosing cancerous cell patterns earlier and faster than
typical screening programs. This paper proposes a computer aided system for automatic detection and
classification of breast cancer in mammogram images. Intuitionistic Fuzzy C-Means clustering technique
has been used to identify the suspicious region or the Region of Interest automatically. Then, the feature
data base is designed using histogram features, Gray Level Concurrence wavelet features and wavelet
energy features. Finally, the feature database is submitted to self-adaptive resource allocation network
classifier for classification of mammogram image as normal, benign or malignant. The proposed system is
verified with 322 mammograms from the Mammographic Image Analysis Society Database. The results
show that the proposed system produces better results.
IRJET- A Novel Segmentation Technique for MRI Brain Tumor ImagesIRJET Journal
This document summarizes several research papers on techniques for segmenting brain tumors in MRI images. It discusses challenges in brain tumor segmentation and describes various approaches that have been proposed, including methods using feature selection, kernel sparse representation, multiple kernel learning (MKL), and post-processing techniques. The document also reviews state-of-the-art segmentation, registration, and modeling methods for brain tumor images and their performance.
A review on detecting brain tumors using deep learning and magnetic resonanc...IJECEIAES
Early detection and treatment in the medical field offer a critical opportunity to survive people. However, the brain has a significant role in human life as it handles most human body activities. Accurate diagnosis of brain tumors dramatically helps speed up the patient's recovery and the cost of treatment. Magnetic resonance imaging (MRI) is a commonly used technique due to the massive progress of artificial intelligence in medicine, machine learning, and recently, deep learning has shown significant results in detecting brain tumors. This review paper is a comprehensive article suitable as a starting point for researchers to demonstrate essential aspects of using deep learning in diagnosing brain tumors. More specifically, it has been restricted to only detecting brain tumors (binary classification as normal or tumor) using MRI datasets in 2020 and 2021. In addition, the paper presents the frequently used datasets, convolutional neural network architectures (standard and designed), and transfer learning techniques. The crucial limitations of applying the deep learning approach, including a lack of datasets, overfitting, and vanishing gradient problems, are also discussed. Finally, alternative solutions for these limitations are obtained.
Similar to Model guided therapy and the role of dicom in surgery (20)
Letter to MREC - application to conduct studyAzreen Aj
Application to conduct study on research title 'Awareness and knowledge of oral cancer and precancer among dental outpatient in Klinik Pergigian Merlimau, Melaka'
TEST BANK For Accounting Information Systems, 3rd Edition by Vernon Richardso...rightmanforbloodline
TEST BANK For Accounting Information Systems, 3rd Edition by Vernon Richardson, Verified Chapters 1 - 18, Complete Newest Version
TEST BANK For Accounting Information Systems, 3rd Edition by Vernon Richardson, Verified Chapters 1 - 18, Complete Newest Version
TEST BANK For Accounting Information Systems, 3rd Edition by Vernon Richardson, Verified Chapters 1 - 18, Complete Newest Version
Unlocking the Secrets to Safe Patient Handling.pdfLift Ability
Furthermore, the time constraints and workload in healthcare settings can make it challenging for caregivers to prioritise safe patient handling Australia practices, leading to shortcuts and increased risks.
At Apollo Hospital, Lucknow, U.P., we provide specialized care for children experiencing dehydration and other symptoms. We also offer NICU & PICU Ambulance Facility Services. Consult our expert today for the best pediatric emergency care.
For More Details:
Map: https://cutt.ly/BwCeflYo
Name: Apollo Hospital
Address: Singar Nagar, LDA Colony, Lucknow, Uttar Pradesh 226012
Phone: 08429021957
Opening Hours: 24X7
Michigan HealthTech Market Map 2024. Includes 7 categories: Policy Makers, Academic Innovation Centers, Digital Health Providers, Healthcare Providers, Payers / Insurance, Device Companies, Life Science Companies, Innovation Accelerators. Developed by the Michigan-Israel Business Accelerator
Gemma Wean- Nutritional solution for Artemiasmuskaan0008
GEMMA Wean is a high end larval co-feeding and weaning diet aimed at Artemia optimisation and is fortified with a high level of proteins and phospholipids. GEMMA Wean provides the early weaned juveniles with dedicated fish nutrition and is an ideal follow on from GEMMA Micro or Artemia.
GEMMA Wean has an optimised nutritional balance and physical quality so that it flows more freely and spreads readily on the water surface. The balance of phospholipid classes to- gether with the production technology based on a low temperature extrusion process improve the physical aspect of the pellets while still retaining the high phospholipid content.
GEMMA Wean is available in 0.1mm, 0.2mm and 0.3mm. There is also a 0.5mm micro-pellet, GEMMA Wean Diamond, which covers the early nursery stage from post-weaning to pre-growing.
PET CT beginners Guide covers some of the underrepresented topics in PET CTMiadAlsulami
This lecture briefly covers some of the underrepresented topics in Molecular imaging with cases , such as:
- Primary pleural tumors and pleural metastases.
- Distinguishing between MPM and Talc Pleurodesis.
- Urological tumors.
- The role of FDG PET in NET.
We are one of the top Massage Spa Ajman Our highly skilled, experienced, and certified massage therapists from different corners of the world are committed to serving you with a soothing and relaxing experience. Luxuriate yourself at our spas in Sharjah and Ajman, which are indeed enriched with an ambiance of relaxation and tranquility. We could confidently claim that we are one of the most affordable Spa Ajman and Sharjah as well, where you can book the massage session of your choice for just 99 AED at any time as we are open 24 hours a day, 7 days a week.
Visit : https://massagespaajman.com/
Call : 052 987 1315
Exploring the Benefits of Binaural Hearing: Why Two Hearing Aids Are Better T...Ear Solutions (ESPL)
Binaural hearing using two hearing aids instead of one offers numerous advantages, including improved sound localization, enhanced sound quality, better speech understanding in noise, reduced listening effort, and greater overall satisfaction. By leveraging the brain’s natural ability to process sound from both ears, binaural hearing aids provide a more balanced, clear, and comfortable hearing experience. If you or a loved one is considering hearing aids, consult with a hearing care professional at Ear Solutions hearing aid clinic in Mumbai to explore the benefits of binaural hearing and determine the best solution for your hearing needs. Embracing binaural hearing can lead to a richer, more engaging auditory experience and significantly improve your quality of life.
The facial nerve, also known as cranial nerve VII, is one of the 12 cranial nerves originating from the brain. It's a mixed nerve, meaning it contains both sensory and motor fibres, and it plays a crucial role in controlling various facial muscles, as well as conveying sensory information from the taste buds on the anterior two-thirds of the tongue.
Joker Wigs has been a one-stop-shop for hair products for over 26 years. We provide high-quality hair wigs, hair extensions, hair toppers, hair patch, and more for both men and women.
U Part Wigs_ A Natural Look with Minimal Effort Jokerwigs.in.pdf
Model guided therapy and the role of dicom in surgery
1. Model-Guided Therapy and the
role of DICOM in Surgery
Heinz U. Lemke, PhD
Chair of Working Group 24 “DICOM in Surgery“
2. Content
1. Introduction (problems and solutions)
2. Model guided therapy with TIMMS
3. Classification and model classes
4. Virtual human model examples
5. Conclusion
3. Computer Assisted Digital OR Suite for Endoscopic MISS
Problems: Multiple Data Sources
Digital endoscopic OR suite facilitates MISS
MD’s
Staff
RN, Tech
EMG
Monitoring
C-Arm
Fluoroscopy
MRI Image -
PACS
C-Arm Images
Image Manager -
Report
Video Endoscopy
Monitor
EEG Monitoring
Left side of OR
Image view
boxes
Teleconferencing
- telesurgery
Laser
generator
Courtesy of Dr. John Chiu
4. Model Guided Therapy and the
Patient Specific Model
• Model Guided Therapy (MGT) is a methodology
complementing Image Guided Therapy (IGT) with
additional vital patient-specific data.
• It brings patient treatment closer to achieving a
more precise diagnosis, a more accurate
assessment of prognosis, as well as a more
individualized planning, execution and validation
of a specific therapy.
• By definition, Model Guided Therapy is based on
a Patient Specific Model (PSM) and allows for a
patient specific intervention via an adapted
therapeutic workflow.
5. Model Guided Therapy and data structures
• Model Guided Therapy based on patient specific
modelling requires appropriate IT architectures
and data structures for its realisation.
• For PSMs, archetypes and templates allow
different levels of generalisation and
specialisation, respectively.
6. Biosensors
(physiology,
metabolism,
serum, tissue, …)
Omics EMR
Modalities
(X-ray,CT, US,
MR,SPECT,
PET,OI)
Model Based Patient Care
EBM
Workflow
IHE
Model Creation
and Diagnosis
(Data fusion,
CAD, …)
Model Maintenance
and Intervention
(Simulation,
decision support,
validation, …)
Data bases
(Atlas,
P2P repositories,
data grids, ...)
Mechatronics
(Navigation,
ablation, …)
IT Communication Infrastructure
7. Content
1. Introduction (problems and solutions)
2. Model guided therapy with TIMMS
3. Classification and model classes
4. Virtual human model examples
5. PM data structures (SDTM and OpenEHR)
6. Conclusion
8. IT Model-Centric World View
Interventional Cockpit/SAS modules
Modelling
Models
(Simulated
Objects)
Therapy Imaging and Model Management System (TIMMS)
ICT infrastructure (based on DICOM-X) for data, image, model and tool communication for patient model-guided therapy
Simulation
Kernel for
WF and K+D
Management
Visualisation
Rep. Manager
Intervention Validation
Repo-
sitory
Engine
Data Exch.
Control
IO Imaging
and
Biosensors
Images
and
signals
Modelling
tools
Computing
tools
WF and
K+D
tools
Rep.
tools
Devices/
Mechatr.
tools
Validation
tools
WF`s, EBM,
”cases”
Data and
information
Models and
intervention
records
Therapy Imaging and Model Management System (TIMMS)
9. Model Guided Therapy with TIMMS
• For a therapeutic intervention it is assumed that
human, mechatronic, radiation or pharmaceutical
agents interact with the model.
• MGT provides the scientific basis for an accurate,
transparent and reproducible intervention with the
potential for validation and other services.
• TIMMS is an IT meta architecture allowing for
interoperability of the agents to facilitate a MGT
intervention.
10. Model Guided Therapy
The basic TIMMS patient model must have the following features:
1. The TIMMS patient model must have components which
represent the patient as an n-dimensional and multiscale
(in space and time) data set.
2. The TIMMS patient model must facilitate interfacing to the
surgeon and other operative personnel, the TIMMS engines,
TIMMS repositories, and the IT infrastructure.
3. The TIMMS patient model must be capable of linking these
components, which may be static or dynamic, in a meaningful
and accurate way.
4. For dynamic components, the TIMMS patient model must be
able to process morphological and physiological data and
perform the necessary mathematical functions to maintain the
model in an up-to-date state.
11. Model Guided Therapy
5. The TIMMS patient model must be capable of being incorporated
by the TIMMS executing workflow and responding to its changes.
6. The TIMMS patient model must be amenable to be developed
using readily available, standardized informatics methodology.
Tools may include UML, XML, Visio, block diagrams, workflow
diagrams, MATLAB, Simulink, DICOM (including surgical DICOM),
Physiome, CDISC SDTM, openEHR and similar products and tools.
7. The TIMMS patient model must comply to software engineering
criteria, for example, to open standards and service-oriented
architectures to allow for multi-disciplinary information exchange.
8. The TIMMS patient model must allow for further extensions to
incorporate advances in molecular medical imaging, genomics,
proteomics and epigenetics.
9. The TIMMS patient model must be amenable to be used for clinical
trials, predictive modeling, personal health records and in the long
term contribute to a Model Based Medical Evidence (EBME)
methodology.
12. IT Model-Centric World View
Interventional Cockpit/SAS modules
Modelling
Models
(Simulated
Objects)
Therapy Imaging and Model Management System (TIMMS)
ICT infrastructure (based on DICOM-X) for data, image, model and tool communication for patient model-guided therapy
Simulation
Kernel for
WF and K+D
Management
Visualisation
Rep. Manager
Intervention Validation
Repo-
sitory
Engine
Data Exch.
Control
IO Imaging
and
Biosensors
Images
and
signals
Modelling
tools
Computing
tools
WF and
K+D
tools
Rep.
tools
Devices/
Mechatr.
tools
Validation
tools
WF`s, EBM,
”cases”
Data and
information
Models and
intervention
records
Therapy Imaging and Model Management System (TIMMS)
13. Generic and patient specific
n-D modelling tools
• Geometric modelling
• Prosthesis modelling
• Properties of cells and tissue
• Segmentation and reconstruction
• Biomechanics and damage
• Tissue growth
• Tissue shift
• Properties of biomaterials
• ...
Modelling
tools
14. Model Guided Therapy
• MGT in its simpliest instantiation is an intervention with
a subset, a single or a set of voxels representing
locations within the patient body. With this view, it is an
extension from Image (pixel) Guided Therapy (IGT) to
model (voxel) guided therapy. Examples of model
guided therapy are:
a) interventions within a subset of a voxel, e.g. cells,
organelles, molecules, etc.
b) interventions with a voxel, e.g. small tissue parts of
an organ or lesion, etc.
c) interventions with a set of voxels, e.g. part of
functional structures of organs, organ components,
soft tissue, lesions, etc.
15. Model Guided Therapy
1. 1-D signals (e.g. EEG)
2. 2-D projection and tomographic images
3. 3-D reconstructions
4. Temporal change
5. Tissue/cell type
6. Ownership to organ, lesion, system, prothesis, chronic
condition, etc.
7. Spatial occupancy/extension
8. Permeability (blood brain barrier)
9. Flow (e.g. electric, heat, liquid, perfusion, diffusion, etc.)
In a simple PSM, voxels may be associated
with several dimensions of data
16. Model Guided Therapy
10. Level of oxygenation (e.g. level of hypoxia)
11. Pharmacokinetics (e.g. effect of tissue on
pharmaceutical agent, flow parameters, time to peak,
etc.)
12. Pharmacodynamics (effect of pharmaceutical agent on
tissue, ablation parameters)
13. Biological marker types (in vitro and/or in vivo
molecular spectrum)
14. Reference coordinate system (e.g.
Schaltenbrand/Warren, Talaraich/Tourneaux)
15. Value (life critical to life threatening)
16. Neighbourhood (e.g. 3³, 5³, 7³, etc.)
17. ...
In a simple PSM, voxels may be associated
with several dimensions of data
19. Content
1. Introduction (problems and solutions)
2. Model guided therapy with TIMMS
3. Classification and model classes
4. Virtual human model examples
5. Conclusion
20. Strategies for multiscale modelling
• Modelling is essential for understanding the
knowledge of human characteristics such as, anatomy,
physiology, metabolism, genomics, proteomics,
pharmacokinetics, etc.
• Because of the complexity of integrating the
knowledge about the different characteristics the
model of a human has to be realised on different
levels (multiscale in space and time) and with different
ontologies, depending on the questions posed and
answered delivered.
• The problems associated with using reduced-form
components within large systems models stem
primarily from their limited range of validity.
22. Patient specific and associated
modelling functions
In the Model-Centric World View a wide variety of
information, relating to the patient, can be integrated
with the images and their derivatives, providing a more
comprehensive and robust view of the patient.
By default, the broader the spectrum of different types of
interventional/surgical workflows which have to be
considered, the more effort has to be given for designing
appropriate multiscale PSM’s and associated services.
23. Patient specific and associated
modelling functions
Management of n-D and multi resolutional
knowledge (model of the biologic continuum in
space and time) is still a research and
development challenge.
If solved successfully, it will transform surgery
into a more scientifically based activity.
24. Content
1. Introduction (problems and solutions)
2. Model guided therapy with TIMMS
3. Classification and model classes
4. Virtual human model examples
5. Conclusion
25. Patient Specific CMB
Visible Human
Anatomical Template
organ surface meshes
Multimodal Imaging
(MRI, CT, Angio,..DT-MRI)
PKPD
Spitzer 2006 Virtual Anatomy
FEM Mesh (Roberts JHU)
Human Laser
Scan (CAESAR DB)
Roberts JHU
26. Content
1. Introduction (problems and solutions)
2. Model guided therapy with TIMMS
3. Classification and model classes
4. Virtual human model examples
5. Conclusion
27. Solutions and Research Focus
(medical)
• Transition from image guided to model guided
therapy (e.g. through workflow and use case
selection/creation/repositories)
• Concepts and specification of patient specific
models in a multiscale domain of discourse
• Concepts and design of a canonical set of low
level surgical functions
• Prototyping
28. IT Model-Centric World View
Interventional Cockpit/SAS modules
Modelling
Models
(Simulated
Objects)
Therapy Imaging and Model Management System (TIMMS)
ICT infrastructure (based on DICOM-X) for data, image, model and tool communication for patient model-guided therapy
Simulation
Kernel for
WF and K+D
Management
Visualisation
Rep. Manager
Intervention Validation
Repo-
sitory
Engine
Data Exch.
Control
IO Imaging
and
Biosensors
Images
and
signals
Modelling
tools
Computing
tools
WF and
K+D
tools
Rep.
tools
Devices/
Mechatr.
tools
Validation
tools
WF`s, EBM,
”cases”
Data and
information
Models and
intervention
records
Therapy Imaging and Model Management System (TIMMS)
Prototyping
29. Solutions and Research Focus
(technical)
• Concepts and data structure design of patient specific
models (e.g. with archetypes and templates)
• Model management with open architectures (e.g. SOA)
• SOA modulariation with repositories, engines, LLM´s and
HLM´s
• LLM´s as adaptive (cognitive/intelligent) agents
• HLM´s as application modules (competitive differentiation)
• LLM´s possibly as open source
• Kernel (engine and repository) for adaptive workflow and
K+D management
• Cooperative and competitive R+D framework for engine
and repository building
• Therapy based open standard ( e.g. S-DICOM)
• Transition from CAD to CAT modelling
30. IT Model-Centric World View
Interventional Cockpit/SAS modules
Modelling
Models
(Simulated
Objects)
Therapy Imaging and Model Management System (TIMMS)
ICT infrastructure (based on DICOM-X) for data, image, model and tool communication for patient model-guided therapy
Simulation
Kernel for
WF and K+D
Management
Visualisation
Rep. Manager
Intervention Validation
Repo-
sitory
Engine
Data Exch.
Control
IO Imaging
and
Biosensors
Images
and
signals
Modelling
tools
Computing
tools
WF and
K+D
tools
Rep.
tools
Devices/
Mechatr.
tools
Validation
tools
WF`s, EBM,
”cases”
Data and
information
Models and
intervention
records
Therapy Imaging and Model Management System (TIMMS)
Archetypes and Templates
31. Solutions and Research Focus
(medical and technical)
• Transition from image guided to model guided therapy (e.g.
through workflow and use case
selection/creation/repositories)
• Use cases for adaptive workflow, exception handling and
K+D management for selected interventions
• Cooperative and competitive R+D framework for low
(open source) and high level (competitive differentiation)
surgical function computerisation
• Information/model flow from diagnosis (e.g. CAD) to CAT
(i.e. interdisciplinary cooperation)
• Development of standards for patient modelling in
WG24 “DICOM in Surgery”
32. IT Model-Centric World View
Interventional Cockpit/SAS modules
Modelling
Models
(Simulated
Objects)
Therapy Imaging and Model Management System (TIMMS)
ICT infrastructure (based on DICOM-X) for data, image, model and tool communication for patient model-guided therapy
Simulation
Kernel for
WF and K+D
Management
Visualisation
Rep. Manager
Intervention Validation
Repo-
sitory
Engine
Data Exch.
Control
IO Imaging
and
Biosensors
Images
and
signals
Modelling
tools
Computing
tools
WF and
K+D
tools
Rep.
tools
Devices/
Mechatr.
tools
Validation
tools
WF`s, EBM,
”cases”
Data and
information
Models and
intervention
records
Candidate components for open source
Open Source
33. WG 24 “DICOM in Surgery“
Project Groups
• PG1 WF/MI Neurosurgery
• PG2 WF/MI ENT and CMF Surgery
• PG3 WF/MI Orthopaedic Surgery
• PG4 WF/MI Cardiovascular Surgery
• PG5 WF/MI Thoraco-abdominal Surgery
• PG6 WF/MI Interventional Radiology
• PG7 WF/MI Anaesthesia
• PG8 S-PACS Functions
• PG9 WFMS Tools
• PG10 Image Processing and Display
• PG11 Ultrasound in Surgery
34. Definition of Surgical Workflows (S-WFs)
• Micro Laryngeal Surgery (MLS) (PG2
ENT/CMF)
• Foreign Body Excision (PG2 ENT/CMF)
• Total Hip Replacement Surgery (PG3
Orthopaedic)
• Total Endoscopic Coronary Artery Bypass (TECAB) (PG4
Cardiovascular)
• Mitral Valve Reconstruction (MVR) (PG4
Cardiovascular)
• Laparoscopic Splenectomy (PG5
Thoraco-abdominal)
• Laparoscopic Cholecystectomy (PG5
Thoraco-abdominal)
• Laparoscopic Nephrectomy left (PG5
Thoraco-abdominal)
• Angiography with PTA and Stent (PG6
Interventional Radiology)
• Hepatic Tumor Radio Frequency Ablation (PG6
Interventional Radiology)
• Trajugular Intrahepatic Portosystemic Shunt (PG6
Interventional Radiology)
35. CARS / SPIE / EuroPACS
9th Joint Workshop on
Surgical PACS and the Digital Operating Room
Barcelona, 28 June, 2008
12th Meeting of the
DICOM Working Group WG 24 “DICOM in Surgery“
Barcelona, 28 June 2008
CARS 2008 Computer Assisted Radiology and Surgery
http://www.cars-int.org
36.
37. WG24 “DICOM in Surgery”
Secretariat: Howard Clark, NEMA
Secretary: Franziska Schweikert, CARS/CURAC Office
fschweikert@cars-int.org
General Chair: Heinz U. Lemke, ISCAS/CURAC, Germany
Co-Chair: Ferenc Jolesz, Harvard Medical School, Boston
(Surgery/Radiology)
Co-Chair: tbd
(Industry)