2017 Spring, UCF Medical Image Computing Course
Basics of Radiological Image Modalities and their clinical use (MRI, PET, CT, fMRI, DTI, ...)
• Introduction to Medical Image Computing and Toolkits
• Image Filtering, Enhancement, Noise Reduction, and
Signal Processing
• MedicalImageRegistration
• MedicalImageSegmentation
• MedicalImageVisualization
• Machine Learning in Medical Imaging
• Shape Modeling/Analysis of Medical Images
Deep Learning in Radiology
Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation
3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms
Volume Identification and Estimation of MRI Brain Tumor
MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier
Lec2: Digital Images and Medical Imaging ModalitiesUlaş Bağcı
2017 Spring, UCF Medical Image Computing Course
X-ray?
• Ultrasound?
• ComputedTomography(CT)?
• MagneticResonanceImaging(MRI)?
• PositronEmissionTomography(PET)? • DiffusionWeightedImaging(DWI)?
• DiffusionTensorImaging(DTI)?
• MagneticParticleImaging(MPI)?
• OpticalCoherenceTomography(OCT)?
Basics of Radiological Image Modalities and their clinical use (MRI, PET, CT, fMRI, DTI, ...) • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation
3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms
Volume Identification and Estimation of MRI Brain Tumor
MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier
Lec2: Digital Images and Medical Imaging ModalitiesUlaş Bağcı
2017 Spring, UCF Medical Image Computing Course
X-ray?
• Ultrasound?
• ComputedTomography(CT)?
• MagneticResonanceImaging(MRI)?
• PositronEmissionTomography(PET)? • DiffusionWeightedImaging(DWI)?
• DiffusionTensorImaging(DTI)?
• MagneticParticleImaging(MPI)?
• OpticalCoherenceTomography(OCT)?
Basics of Radiological Image Modalities and their clinical use (MRI, PET, CT, fMRI, DTI, ...) • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
2017 Spring, UCF Medical Image Computing
CAVA: Computer Aided Visualization and Analysis
• CAD: Computer Aided Diagnosis
• Definitions and Terminologies
• Coordinate Systems
• Pre-Processing Images – Volume of Interest
– RegionofInterest
– IntensityofInterest – ImageEnhancement
• Filtering
• Smoothing
• Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Lec5: Pre-Processing Medical Images (III) (MRI Intensity Standardization)Ulaş Bağcı
2017 Spring, UCF Medical Image Computing CAVA: Computer Aided Visualization and Analysis • CAD: Computer Aided Diagnosis • Definitions and Terminologies • Coordinate Systems • Pre-Processing Images – Volume of Interest – RegionofInterest – IntensityofInterest – ImageEnhancement • Filtering • Smoothing • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
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.
An introduction to Digital Image Processing as a continuation of a classic Digital Signal Processing course delivered at the University of Plymouth (2011)
Lec8: Medical Image Segmentation (II) (Region Growing/Merging)Ulaş Bağcı
. Region Growing algorithm
• Homogeneity Criteria
• Split/Merge algorithm
• Examples from CT, MRI, PET
• Limitations
Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Lec4: Pre-Processing Medical Images (II)Ulaş Bağcı
2017 Spring, UCF Medical Image Computing CAVA: Computer Aided Visualization and Analysis • CAD: Computer Aided Diagnosis • Definitions and Terminologies • Coordinate Systems • Pre-Processing Images – Volume of Interest – RegionofInterest – IntensityofInterest – ImageEnhancement • Filtering • Smoothing • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Lec13: Clustering Based Medical Image Segmentation MethodsUlaş Bağcı
Clustering – K-means
– FCM (fuzzyc-means)
– SMC (simple membership based clustering) – AP(affinity propagation)
– FLAB(fuzzy locally adaptive Bayesian)
– Spectral Clustering Methods
ShapeModeling – M-reps – Active Shape Models (ASM) – Oriented Active Shape Models (OASM) – Application in anatomy recognition and segmentation – Comparison of ASM and OASM ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Image segmentation techniques
More information on this research can be found in:
Hussein, Rania, Frederic D. McKenzie. “Identifying Ambiguous Prostate Gland Contours from Histology Using Capsule Shape Information and Least Squares Curve Fitting.” The International Journal of Computer Assisted Radiology and Surgery ( IJCARS), Volume 2 Numbers 3-4, pp. 143-150, December 2007.
Biomedical Image Processing
Topics covered: Biomedical imaging, Need of image processing in medicine, Principles of image processing, Components of image processing, Application of image processing in different medical imaging systems
In this presentation we described important things about Image processing and computer vision. If you have any query about this presentation then feels free to visit us at:
http://www.siliconmentor.com/
This slide best explains the introduction of CT, basis and types of CT image reconstructions with detailed explanation about Interpolation, convolution, Fourier slice theorem, Fourier transformation and brief explanation about the image domain i.e digital image processing.
In this talk I'll discuss work in biomedical image and volume segmentation and classification, as well as outcome prediction modeling from insurance claims data that I've pursued at LifeOmic here in the Triangle. In the former case datasets include radiological image volumes, retinal fundus images, and cell images created with fluorescent microscopy. The latter includes MIMIC-III data represented as FHIR objects. I'll discuss the relative challenges and advantages of doing ML locally vs. on a cloud-based platform.
2017 Spring, UCF Medical Image Computing
CAVA: Computer Aided Visualization and Analysis
• CAD: Computer Aided Diagnosis
• Definitions and Terminologies
• Coordinate Systems
• Pre-Processing Images – Volume of Interest
– RegionofInterest
– IntensityofInterest – ImageEnhancement
• Filtering
• Smoothing
• Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Lec5: Pre-Processing Medical Images (III) (MRI Intensity Standardization)Ulaş Bağcı
2017 Spring, UCF Medical Image Computing CAVA: Computer Aided Visualization and Analysis • CAD: Computer Aided Diagnosis • Definitions and Terminologies • Coordinate Systems • Pre-Processing Images – Volume of Interest – RegionofInterest – IntensityofInterest – ImageEnhancement • Filtering • Smoothing • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
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.
An introduction to Digital Image Processing as a continuation of a classic Digital Signal Processing course delivered at the University of Plymouth (2011)
Lec8: Medical Image Segmentation (II) (Region Growing/Merging)Ulaş Bağcı
. Region Growing algorithm
• Homogeneity Criteria
• Split/Merge algorithm
• Examples from CT, MRI, PET
• Limitations
Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Lec4: Pre-Processing Medical Images (II)Ulaş Bağcı
2017 Spring, UCF Medical Image Computing CAVA: Computer Aided Visualization and Analysis • CAD: Computer Aided Diagnosis • Definitions and Terminologies • Coordinate Systems • Pre-Processing Images – Volume of Interest – RegionofInterest – IntensityofInterest – ImageEnhancement • Filtering • Smoothing • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Lec13: Clustering Based Medical Image Segmentation MethodsUlaş Bağcı
Clustering – K-means
– FCM (fuzzyc-means)
– SMC (simple membership based clustering) – AP(affinity propagation)
– FLAB(fuzzy locally adaptive Bayesian)
– Spectral Clustering Methods
ShapeModeling – M-reps – Active Shape Models (ASM) – Oriented Active Shape Models (OASM) – Application in anatomy recognition and segmentation – Comparison of ASM and OASM ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Image segmentation techniques
More information on this research can be found in:
Hussein, Rania, Frederic D. McKenzie. “Identifying Ambiguous Prostate Gland Contours from Histology Using Capsule Shape Information and Least Squares Curve Fitting.” The International Journal of Computer Assisted Radiology and Surgery ( IJCARS), Volume 2 Numbers 3-4, pp. 143-150, December 2007.
Biomedical Image Processing
Topics covered: Biomedical imaging, Need of image processing in medicine, Principles of image processing, Components of image processing, Application of image processing in different medical imaging systems
In this presentation we described important things about Image processing and computer vision. If you have any query about this presentation then feels free to visit us at:
http://www.siliconmentor.com/
This slide best explains the introduction of CT, basis and types of CT image reconstructions with detailed explanation about Interpolation, convolution, Fourier slice theorem, Fourier transformation and brief explanation about the image domain i.e digital image processing.
In this talk I'll discuss work in biomedical image and volume segmentation and classification, as well as outcome prediction modeling from insurance claims data that I've pursued at LifeOmic here in the Triangle. In the former case datasets include radiological image volumes, retinal fundus images, and cell images created with fluorescent microscopy. The latter includes MIMIC-III data represented as FHIR objects. I'll discuss the relative challenges and advantages of doing ML locally vs. on a cloud-based platform.
For internal meeting of the Executive Committee of Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University
At the Seventh Annual Health Law Year in P/Review symposium, leading experts discussed major developments during 2018 and what to watch out for in 2019. Speakers covered hot topics including health policy under the current administration, pharmaceutical policy, and public health law. Featured panels explored "Challenges Facing Health Care General Counsels" and "AI in Health Care."
For more, go to: http://petrieflom.law.harvard.edu/events/details/seventh-annual-health-law-year-in-p-review
Conferencia: "Superando la brecha entre Investigación y Aplicación", a cargo del Dr. Ander Ramos, investigador de TECNALIA.
TECNALIA Perspectives 2015. “Industria y Tecnología: Investigación traslacional, de la ciencia al mercado” es el título del evento que contó con la participación del Dr. Niels Birbaumer, experto mundial en el desarrollo de interfaces cerebro-computador y del investigador Dr. Ander Ramos, Premio al Mejor Investigador Joven de Alemania en 2014 por la Academia Alemana para las Ciencias y las Letras.
También presentamos dos novedosas iniciativas de TECNALIA como son un dispositivo de estimulación eléctrica funcional para rehabilitación de pacientes de ictus y el robot quirúrgico con visión 3D y sensaciones táctiles.
Más información en http://www.tecnalia.com
On March 23, 2016, Prof. Henning Müller (HES-SO Valais-Wallis and Martinos Center) presented Medical image analysis and big data evaluation infrastructures at Stanford medicine.
Utility and Added Value of Classifications in Health Information SystemsBedirhan Ustun
Health Information Systems; ICD, ICD11, SNOMED-CT, Use Cases showing benefits of use of classification- terminology systems; avoid and e-tower of Babel; electronic health record, Enhance Patient Care, Decision Support, Safety & Quality
In this presentation I share the ideas regarding Radiology and AI relation to each other . Its helps to explore more about radiology . Its key advaantage is that u find a detailed knowledge of Radio Imaging and AI at the same platform . If u want to know about the healthcare and research centers where AI is used you also find the collabration among various AI TECHNOLOGY MANUFACTURERS and RESEARCH CENTERS . If you are doing graduation in AI or RADIOLOGY field , it well define your stream . It enhance the workfield arena for radigraphers and how they will increase their job profiles and clearly differntiate them between Robots and AI because most of our assests thought AI in healthcare leads to robotic work but they are totally wrong in this , they need to understand that AI is just going to decrease the work pressure on them . This will not going to take their jobs and our radio imaging machines are only operated by our medical proffesssionalists i.e. our Radiotechnician . It will reduce the waiting time for patients and it increase the job satisfaction for us. As a Radio Imaging student , I try to clear all the doubts regarding the job misconceptiopns in our mind. We must be prgressive , truthful and honest while our job responsibility. AI will help in faster Image Analysis , it helps in Diagnostic accuracy, it also helps in early detection of disease , as it will pre analysize the data from various modalities and make a 3D view of the image form . It leads to increase the patients trust on our healthcaere providers . It helps in unnecessary excessive radiation to the patients , leads to decrease in the risk of radiation related diseases , like skin erethema , skin cancer and skin infections . Yeah.. you also find some challenges regarding smart radiology but by doing proper cincern in your imaging field you will going to solve all the errors in it . We must try to organize short confrence in which we share our views and plans for future innovations and this presentation is small effort towards it. It will also control the quality of your images . This presentation will help you differntiate between the TELEMEDICINE and PACS . I also share the requireds tools and techniques which are using in radiological field . Some of them are - machine learning, deep learning, natural language processing, computer aided detection, image segmentation, 3D- Imaging analysis, automated repoting , radiomics, generative adversial networks . It will show you how AI effects life of Radiographers and Technicians , by stating : efficiency and productivity, workload management , skill enhancement, career evolution, job satisfaction, and patients care, quality assurance. Here , I also share some case studies where AI ia implemented in Radiology, it includes Cleveland Clinic collabration with Zebra Medical Vision , Stanford Medicine use of ARTERYS and UNIVERSITY OF CALIFORNIA , SAN FRANCISCO [ UCSF] and TEMPU . Whereas some challenges in adopting AI in Radiology.
Greetings from ACS!
After the series of successful courses on PACS, ACS is closely working with two expert trainers, Mr. Andrea Poli, PACS Implementation expert from Italy, and Mr. Ronald Gilbert, senior Imaging and Informatics practitioner and Instructor from US, to organize our upcoming 05 days professional course “PACS AND IMAGING INFORMATICS”, with ASRT approved 37.50 Category A CE credits.
This course is scheduled from March 26th to March 30th, 2018 at Aloft Kuala Lumpur Sentral, Kuala Lumpur, Malaysia.
The course is great chance to prepare for CIIP exam.
In case of any inquiry, please feel free to contact Mr. Syed at training@acsmb.com
Thank you.
A Lightning Round presentation for the University of Michigan's Emergent Research Series, July 23, 2016, presenting recent work from the MLA Systematic Review Team 6 on Emerging Technologies.
Similar to Lec1: Medical Image Computing - Introduction (20)
Lec14: Evaluation Framework for Medical Image SegmentationUlaş Bağcı
How to evaluate accuracy of image segmentation?
– Gold standard ~ surrogate of truths
– Qualitative • Visual
• Inter-andintra-observeragreementrates – Quantitative
• Volumetricmeasurements(regression) • Regionoverlaps
• Shapebasedmeasurements
• Theoreticalcomparisons
• STAPLE,Uncertaintyguidance,andevaluationw/otruths
Clustering – K-means – FCM (fuzzyc-means) – SMC (simple membership based clustering) – AP(affinity propagation) – FLAB(fuzzy locally adaptive Bayesian) – Spectral Clustering Methods ShapeModeling – M-reps – Active Shape Models (ASM) – Oriented Active Shape Models (OASM) – Application in anatomy recognition and segmentation – Comparison of ASM and OASM ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Lec12: Shape Models and Medical Image SegmentationUlaş Bağcı
ShapeModeling – M-reps
– Active Shape Models (ASM)
– Oriented Active Shape Models (OASM)
– Application in anatomy recognition and segmentation – Comparison of ASM and OASM
ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Lec11: Active Contour and Level Set for Medical Image SegmentationUlaş Bağcı
ActiveContour(Snake) • LevelSet
• Applications
Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energyfunctional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
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Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method
Energyfunctional
– Data and Smoothness terms
• GraphCut – Min cut
– Max Flow
• ApplicationsinRadiologyImages
Lec9: Medical Image Segmentation (III) (Fuzzy Connected Image Segmentation)Ulaş Bağcı
Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Fuzzy Connectivity (FC) – Affinity functions
• Absolute FC
• Relative FC (and Iterative Relative FC)
• Successful example applications of FC in medical imaging
• Segmentation of Airway and Airway Walls using RFC based method
Lec6: Pre-Processing for Nuclear Medicine ImagesUlaş Bağcı
2017 Spring, UCF Medical Image Computing
1. The use of PET/SPECT, PET/CT and MRI/PET Images
2. What to measure from Nuclear Medicine Images?
3. Denoising Nuclear MedicineI mages
4. PartialVolumeCorrection
ImageEnhancement • Filtering • Smoothing • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
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Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...
Lec1: Medical Image Computing - Introduction
1. MEDICAL IMAGE COMPUTING (CAP 5937)- SPRING 2017
LECTURE 1: Introduction
Dr. Ulas Bagci
HEC 221, Center for Research in Computer Vision
(CRCV), University of Central Florida (UCF),
Orlando, FL 32814.
bagci@ucf.edu or bagci@crcv.ucf.edu
1
2. • This is a special
topics course,
offered for the
second time in UCF.
2
CAP5937: Medical Image Computing
Lorem Ipsum Dolor Sit Amet
3. • This is a special
topics course,
offered for the
second time in UCF.
• Lectures:
Mon/Wed, 10.30am-
11.45am
3
CAP5937: Medical Image Computing
Lorem Ipsum Dolor Sit Amet
4. • This is a special
topics course,
offered for the
second time in UCF.
• Lectures:
Mon/Wed, 10.30am-
11.45am
• Office hours:
Mon/Wed, 1pm-
2.30pm
4
CAP5937: Medical Image Computing
Lorem Ipsum Dolor Sit Amet
5. • This is a special topics
course, offered for the
second time in UCF.
• Lectures: Mon/Wed,
10.30am-11.45am
• Office hours:
Mon/Wed, 1pm-
2.30pm
• No textbook is
required, materials will
be provided.
• Avg. grade was A- last
spring.
5
CAP5937: Medical Image Computing
Lorem Ipsum Dolor Sit Amet
7. Motivation
• Imaging sciences is experiencing a tremendous
growth in the U.S. The NYT recently ranked
biomedical jobs as the number one fastest growing
career field in the nation and listed bio-medical
imaging as the primary reason for the growth.
7
8. Motivation
• Imaging sciences is experiencing a tremendous
growth in the U.S. The NYT recently ranked
biomedical jobs as the number one fastest growing
career field in the nation and listed bio-medical
imaging as the primary reason for the growth.
• Biomedical imaging and its analysis are fundamental
to (1) understanding, (2) visualizing, and (3)
quantifying information.
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9. Motivation
• Imaging sciences is experiencing a tremendous
growth in the U.S. The NYT recently ranked
biomedical jobs as the number one fastest growing
career field in the nation and listed bio-medical
imaging as the primary reason for the growth.
• Biomedical imaging and its analysis are fundamental
to (1) understanding, (2) visualizing, and (3)
quantifying information.
• This course will mostly focus on analysis of
biomedical images, and imaging part will be briefly
taught!
9
10. Syllabus
• Basics of Radiological Image Modalities and their
clinical use (MRI, PET, CT, fMRI, DTI, …)
• Introduction to Medical Image Computing and Toolkits
• Image Filtering, Enhancement, Noise Reduction, and
Signal Processing
• Medical Image Registration
• Medical Image Segmentation
• Medical Image Visualization
• Machine Learning in Medical Imaging
• Shape Modeling/Analysis of Medical Images
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11. Syllabus
• Grading:
– In-class Quiz (20%, approximately 20 quizzes, at the end of
each lecture)
– 3 ProgrammingAssignments (each 10%, total 30%)
• ITK/VTK packages should be used
• ITK and VTK provide necessary codes/libraries for medical image
processing and analysis.
• C/C++ or Python can be used and call ITK/VTK functions
• In-class collaboration is encouraged, but individual submission is
required.
– 1 Individual Project (50%)
• Will be selected from a list of projects or you can come withh your
own project
• A short presentation (15%), coding/method (25%), results (10%)
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12. Optional Reading List
• Image Processing, Analysis, and MachineVision. M. Sonka, V.
Hlavac, R. Boyle. Nelson Engineering, 2014.
• Level-set Methods, by J. A. Sethian, Cambridge UniversityPress.
• Visual Computingfor Medicine: Theory, Algorithms, and
Applications. B.Preim, C. Botha. Morgan Kaufmann, 2013.
• Medical Image Registration. J. Hajnal, D. Hill, D. Hawkes (eds).
CRC Press, 2001.
• Pattern Recognition and MachineLearning. C. Bishop. Springer,
2007.
• Insight into Images: Principles and Practice for Segmentation,
Registration and Image Analysis, TerryS. Yoo (Editor) (FREE)
• Algorithms for Image Processingand ComputerVision, J. R. Parker
• Medical ImagingSignals and Systems, by Jerry Prince & Jonathan
Links, Publisher: Prentice Hall
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13. Conferences and Journals to Follow
• The top-tier conferences (double blind, acceptance rates are below
25%, high quality technical articles):
– MICCAI (medical image computing & computer assisted intervention)
– IPMI (Information Processing in Medical Imaging)
– Other conferences: IEEE ISBI, EMBC and SPIE Med Imaging
– Clinical Conferences: RSNA (>65.000 attendances), ISMRM, SNM
• The top-tier technical journals:
– IEEE TMI, TBME, PAMI, and TIP
– Medical Image Analysis, CMIG, and NeuroImage
• The top-tier clinical journalsrelevant to MIC:
– Radiology, Journal of Nuclear Medicine, AJR, Nature Methods, Nature
Medicine, PlosOne, …
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14. Required skill set
• Basic programming experience (any language is fine)
• Linear Algebra/Matrix Algebra
• Differential Equations
• Basic Statistics
14
16. Biomedical Images
• (Bio)medical images are different from other
pictures
– They depict distributions of various physical features
measured from the human body (or animal body).
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17. Biomedical Images
• (Bio)medical images are different from other
pictures
– They depict distributions of various physical features
measured from the human body (or animal body).
• Analysis of biomedical images is guided by very
specific expectations
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18. Biomedical Images
• (Bio)medical images are different from other pictures
– They depict distributions of various physical features
measuredfrom the human body (or animal).
• Analysis of biomedical images is guided by very
specific expectations
– Automatic detection of tumors, characterizing their types,
– Measurementof normal/abnormal structures,
– Visualization of anatomy, surgery guidance, therapy
planning,
– Exploring relationship betweenclinical, genomic, and
imaging based markers
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19. Free Software to use in this course
• ImageJ (and/or FIJI)
• ITK-Snap
• SimpleITK
• MITK
• FreeSurfer
• SLICER
• OsiriX
• An extensive list of software: www.idoimaging.com and
blue: will be frequently used in this course
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21. DICOM (the mostly used)
• Digital Imaging and Communicationsin Medicine standard
• Since its first publicationin 1993, DICOM has revolutionized
the practice of radiology, allowingthe replacement of X-ray
film with a fully digital workflow.
• It is the internationalstandard for medical images and related
information(ISO 12052)
• defines the formats for medical images that can be exchanged
with the data and quality necessaryfor clinical use.
• It is implemented in almost every radiology,cardiology
imaging, and radiotherapydevice (X-ray, CT, MRI, ultrasound,
etc.), and increasinglyin devices in other medical domains
such as ophthalmology anddentistry.
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22. 3D Slicer Software
• A software platform for the analysis (including
registration and interactive segmentation) and
visualization (including volume rendering) of
medical images and for research in image guided
therapy.
• A free, open source software available on multiple
operating systems: Linux, MacOSX and Windows
• Extensible, with powerful plug-in capabilities for
adding algorithms and applications.
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23. Brief History of 3D Slicer
• 1997: Slicer started
as a research
project between the
Surgical Planning
Lab (Harvard) and
the CSAIL (MIT)
• Open Source +
Open Data + Open
Community
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•80 authorized developers
contributing to the source
code of Slicer
35. Libraries to be Used
• ITK and VTK
– National Library of
Medicine Insight
Segmentation and
RegistrationToolkit
(ITK).
– ITK is an open-source,
cross-platform system that
provides developers with
an extensive suite of
software tools for image
analysis.
– C/C++,Python, Matlab,
…
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36. Goals of ITK
– Supporting the Visible Human Project.
– Establishing a foundation for future research.
– Creating a repository of fundamental algorithms.
– Developing a platform for advanced product
development.
– Support commercial application of the technology.
– Create conventions for future work.
– Grow a self-sustaining community of software users and
developers.
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37. History of ITK
• ITK was initially conceived by the NLM(National
Library of Medicine).
• An initiative for open source software tools to
analyze human dataset
• Developed by group of both commercial and
academic organizations(kitware, GE research,
Mathsoft, Upenn, UT, UNC
• Goal: provide a foundation to enable research in
image processing and biomedical image computing,
Providing catalog of algorithms
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38. What is ITK?
• Image Processing
• Segmentation
• Registration
• No Graphical User Interface (GUI)
• No Visualization
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39. Coordinate System for Reading Files
• Multiple coordinate frames
– Physical
– Patient
– Index
• ITK uses LPS (Left Posterior Superior)
for DICOM
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42. Installation process
• Google ITK , go to the download page, download
the zip file (or directly install using github or console
functions in mac/linux)
• Google cmake, go to the download page, get the
binaries and install the binaries.
43. Configuring ITK – MS-Windows
l Run CMake
l Select the SOURCE directory
l Select the BINARY directory
l Select your Compiler
45. Configuring ITK
l Disable BUILD_EXAMPLES
l Disable BUILD_SHARED_LIBS
l Disable BUILD_TESTING
l Click “Configure” to configure
l Click “OK” to generate project files
46. Building ITK
l Open ITK.sln in the Binary Directory
l Select ALL_BUILD project
l Build it
…It will take about 15 minutes …
47. Verify the Built
Libraries will be found in
ITK_BINARY / bin / { Debug, Release }
• The following libraries should be there:
– ITKCommon
– ITKBasicFilters
– ITKAlgorithms
– ITKNumerics
– ITKFEM
ITKIO
ITKStatistics
ITKMetaIO
itkpng
itkzlib
48. Use ITK from an external Project
Copy
“HelloWorld.cxx”
“CMakeLists.txt”
from the
Examples/Installation
Directory
into another
directory
Run
CMake
• Select Source Dir
• Select Binary Dir
49. Use ITK from an external Project
• accept the default in
CMAKE_BACKBARD_COMPATIBILITY
• leave empty EXECUTABLE_OUTPUT_PATH
• leave empty LIBRARY_OUTPUT_PATH
• Set ITK_DIR to the binary directory
where ITK was built
50. Build Sample Project
• Open HelloWorld.sln generated by CMake
• Select ALL_BUILD project
• Build it
51. Run the example
• Locate the file HelloWorld.exe
• Run it…
• It should produce the message:
ITK Hello World !
52. Starting your own project
• Create a clean new directory
• Write a CMakeLists.txtfile
• Write a simple .cxx file
• Configure with CMake
• Build
• Run
56. How to find what you need?
• Follow the link Alphabetical List
• Follow the link Groups
• Post to the insight-users mailing list
http://www.itk.org/ItkSoftwareGuide.pdf
http://www.itk.org/Doxygen/html/index.html
57.
58.
59. VTK
• The Visualization Toolkit (VTK) is an open-
source, freely available software system for 3D
computer graphics, image processing, and
visualization.
• Consists of a C++ class library and several
interpreted interface layers including Tcl/Tk, Java,
and Python
60. VTK
• Download VTK from vtk.org
• Configure VTK
– Run CMake
– Select the SOURCE directory
– Select the BINARY directory
– Select your Compiler (same used for ITK)
63. Build VTK
• Open VTK.dswin the Binary Directory
• Select ALL_BUILD project
• Build it
…It may take about 90 minutes …
Verify the Build
Libraries will be found in
VTK_BINARY / bin/ { Debug, Release}
64. Verify the Build
The following libraries
should be there:
• vtkCommon
• vtkFiltering
• vtkImaging
• vtkGraphics
• vtkHybrid
• vtkParallel
• vtkPatented
• Vtkexpat
• Vtkfreetype
• Vtkftgl
• Vtkjpeg
• Vtkpng
• Vtktiff
• vtkzlib
65. Starting your own project
with ITK + VTK
• Create a clean new directory
• Write a CmakeLists.txtfile
• Write a simple .cxxfile
• Configure with CMake
• Build
• Run
76. SimpleITK
• New Wrapper for the insight segmentation &
registration toolkit
• Goal :to help rapid prototyping and expand the user
based of ITK by exposing the algorithms to new users
• Simplify the algorithms so they don’t depend on types
of images.
• Binary built in distributions
• Supports 2D & 3D image , multi component images
• Easy importing & exporting
• data through Numpy