This document summarizes an automatic left ventricle segmentation technique using iterative thresholding and an active contour model adapted for short-axis cardiac MRI images. It begins with background on image segmentation and its applications. Then, it reviews related work on cardiac segmentation techniques and their limitations. The proposed method segments the endocardium using iterative thresholding and the epicardium using an active contour model. It estimates blood and myocardial intensities, applies region growing to segment the endocardium in each slice, and propagates the segmentation to remaining slices. Finally, it measures left ventricle volume and compares the results to manual segmentation.
The document provides an overview of first aid techniques for common injuries. It defines first aid as helping an injured or sick person until medical help arrives to prevent further harm. Key areas covered include checking breathing and circulation, treating bleeding, shock, fractures, wounds, burns, poisoning, simple injuries, and safe transportation of casualties. Proper first aid equipment and supplies for first aid kits are also outlined.
El documento describe los diferentes ejes y vistas utilizadas en ecocardiografía para examinar las estructuras cardiacas. Explica que el eje largo examina la aorta y válvula aórtica, mientras que el eje corto permite ver las cuatro cámaras cardiacas desde diferentes ángulos. También describe las vistas apicales de dos, cuatro y cinco cámaras, así como las vistas subcostal y supraesternal. Finalmente, define los tractos de salida del ventrículo izquierdo y derecho.
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This document summarizes an automatic left ventricle segmentation technique using iterative thresholding and an active contour model adapted for short-axis cardiac MRI images. It begins with background on image segmentation and its applications. Then, it reviews related work on cardiac segmentation techniques and their limitations. The proposed method segments the endocardium using iterative thresholding and the epicardium using an active contour model. It estimates blood and myocardial intensities, applies region growing to segment the endocardium in each slice, and propagates the segmentation to remaining slices. Finally, it measures left ventricle volume and compares the results to manual segmentation.
The document provides an overview of first aid techniques for common injuries. It defines first aid as helping an injured or sick person until medical help arrives to prevent further harm. Key areas covered include checking breathing and circulation, treating bleeding, shock, fractures, wounds, burns, poisoning, simple injuries, and safe transportation of casualties. Proper first aid equipment and supplies for first aid kits are also outlined.
El documento describe los diferentes ejes y vistas utilizadas en ecocardiografía para examinar las estructuras cardiacas. Explica que el eje largo examina la aorta y válvula aórtica, mientras que el eje corto permite ver las cuatro cámaras cardiacas desde diferentes ángulos. También describe las vistas apicales de dos, cuatro y cinco cámaras, así como las vistas subcostal y supraesternal. Finalmente, define los tractos de salida del ventrículo izquierdo y derecho.
The document provides instructions for positioning and planning various cardiac MRI scans, including 2D, 3D, and 4D views of the heart. It describes how to position slices on orthogonal localizers to visualize specific anatomical structures like the ventricles, valves, and septum. Views covered include the 4-chamber view, 2-chamber left ventricular view, short axis view, 3-chamber view, and planned views of the left ventricular outflow tract, aortic valve, mitral valve, and right ventricular outflow tract.
The document summarizes the anatomy of the heart in three parts. It begins by describing the location, size, and external features of the heart. It then explains the internal structures of the heart including the layers of the heart wall, the four chambers, and the valves. It concludes by detailing the circulation of blood through the heart and lungs via the major vessels and coronary arteries.
This document provides an introduction to image segmentation. It discusses how image segmentation partitions an image into meaningful regions based on measurements like greyscale, color, texture, depth, or motion. Segmentation is often an initial step in image understanding and has applications in identifying objects, guiding robots, and video compression. The document describes thresholding and clustering as two common segmentation techniques and provides examples of segmentation based on greyscale, texture, motion, depth, and optical flow. It also discusses region-growing, edge-based, and active contour model approaches to segmentation.
This document provides instructions for a game called "Bottle-in-hangers" that can be played with friends. To play, each player stands behind a line and takes turns throwing hangers at a bottle, trying to get the hanger's hole to fit around the bottle. Players keep throwing hangers until they are all thrown. Then players count how many hangers have trapped the bottle. The winner is the player who trapped the most bottles in the least amount of time.
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The bagpipe was not originally from Scotland, but evolved into the Great Highland Bagpipe that is now widely regarded as Scotland's national instrument. While the harp was previously the national instrument, bagpipes rose to prominence in the 15th century as the instrument of Scottish clans and highlanders. Bagpipes originated in ancient Egypt and spread throughout Europe in various forms before developing into the three-drone version most closely associated with Scottish identity over centuries of use by Scottish clans. However, Scotland lacked a national collection of bagpipes, which has led to gaps in documenting their history as the country's national instrument.
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Despite these headwinds, industry leaders and investors remain confident that cell therapies are poised to address significant unmet need in patients suffering from autoimmune disorders. However, the extent of this impact on the treatment landscape remains to be seen, as the industry rapidly approaches an inflection point.
Kosmoderma Academy, a leading institution in the field of dermatology and aesthetics, offers comprehensive courses in cosmetology and trichology. Our specialized courses on PRP (Hair), DR+Growth Factor, GFC, and Qr678 are designed to equip practitioners with advanced skills and knowledge to excel in hair restoration and growth treatments.
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2. What is MI?
Introduction What is DE-MRI?
Problem definition
What is myocardial infarction (MI)?
Heart attack, caused by coronary arthrosclerosis
Myocardium: heart muscle
Infarction: tissue death, due to
lack of oxygen
Heterogeneous infarct zones
(HIA):
Infarct core
Peri-infarct
Microvascular obstruction
(no-reflow)
3. What is MI?
What is DE-MRI?
Introduction Problem definition
Delay-enhancement MRI (DE-MRI)
• Gold standard for MI viability
• 10 – 15 minutes after contrast agent injection
• Infarct area is shown as hyper-enhancement
Acquisition of DE-MRI slices
4. What is MI?
What is DE-MRI?
Introduction Problem definition
Delay-enhancement MRI (DE-MRI)
• Gold standard for MI viability
• 10 – 15 minutes after contrast agent injection
• Infarct area is shown as hyper-enhancement Endocardium
Acquisition of DE-MRI slices
5. What is MI?
What is DE-MRI?
Introduction Problem definition
Delay-enhancement MRI (DE-MRI)
• Gold standard for MI viability
• 10 – 15 minutes after contrast agent injection
Epicardium
• Infarct area is shown as hyper-enhancement Endocardium
Acquisition of DE-MRI slices
6. What is MI?
What is DE-MRI?
Introduction Problem definition
Delay-enhancement MRI (DE-MRI)
• Gold standard for MI viability
• 10 – 15 minutes after contrast agent injection
Epicardium
• Infarct area is shown as hyper-enhancement Endocardium
Infarct in DE-MRI
Acquisition of DE-MRI slices
7. What is MI?
What is DE-MRI?
Introduction Problem definition
Problem Definition
HIA is hard to be distinguished visually
No automatic solution available
8. What is MI?
What is DE-MRI?
Introduction Problem definition
Problem Definition
HIA is hard to be distinguished visually
No automatic solution available
Project goals
! Develop automatic segmentation and quantification methods, by
taking into account HIA.
! Implement clinical software for automatic quantification of MI from
DE-MR images
9. Infarct segmentation
HIA segmentation
State of the Art Quantification &
Representation
• Infarct segmentation
Diagram + pictures
Kim, et al (1999)
Beek, et al (2005, 2009)
Heiberg, et al (2005, 2008)
10. Infarct segmentation
HIA segmentation
State of the Art Quantification &
Representation
• Infarct segmentation
Diagram + pictures
Kim, et al (1999) Amado, et al (2004)
Beek, et al (2005, 2009)
Heiberg, et al (2005, 2008)
11. Infarct segmentation
HIA segmentation
State of the Art Quantification &
Representation
• Infarct segmentation
Diagram + pictures
Kim, et al (1999) Hsu, et al (2006)
Amado, et al (2004)
Beek, et al (2005, 2009)
Heiberg, et al (2005, 2008)
12. Infarct segmentation
HIA segmentation
State of the Art Quantification &
Representation
• Infarct segmentation
Diagram + pictures
Kim, et al (1999) Hsu, et al (2006)
Amado, et al (2004)
Beek, et al (2005, 2009)
Heiberg, et al (2005, 2008)
Friman, et al (2008)
Doublier, et al (2003)
Metwally, et al (2010)
Hennemuth, et al (2008)
Elagoumi, et al (2010)
13. Infarct segmentation
HIA segmentation
State of the Art Quantification &
Representation
• Infarct segmentation
Diagram + pictures
Kim, et al (1999) Hsu, et al (2006)
Amado, et al (2004)
Beek, et al (2005, 2009)
Heiberg, et al (2005, 2008)
Friman, et al (2008)
Doublier, et al (2003)
Metwally, et al (2010)
Hennemuth, et al (2008)
Elagoumi, et al (2010)
14. Infarct segmentation
HIA segmentation
State of the Art Quantification &
Representation
HIA Segmentation
Simple intensity thresholding Microvascular obstruction (MO)
Yan, et al (2006) – SD based
Schmidt, et al (2007) – FWHM based NO exact threshold definition
Hundley, et al (2010) – SD based
15. Infarct segmentation
HIA segmentation
State of the Art Quantification &
Representation
HIA Segmentation
Infarct core Peri-Infarct
Simple intensity thresholding Microvascular obstruction (MO)
Yan, et al (2006) – SD based
Schmidt, et al (2007) – FWHM based NO exact threshold definition
Hundley, et al (2010) – SD based
16. Infarct segmentation
HIA segmentation
State of the Art Quantification &
Representation
HIA Segmentation
No-reflow
Infarct core Peri-Infarct
Simple intensity thresholding Microvascular obstruction (MO)
Yan, et al (2006) – SD based
Schmidt, et al (2007) – FWHM based NO exact threshold definition
Hundley, et al (2010) – SD based
17. Infarct segmentation
HIA segmentation
State of the Art Quantification &
Representation
Quantification Representation
Bull’s eye plot in
Contiguous short-axis slices 17-segment
model
Slice thickness
18. Infarct segmentation
HIA segmentation
State of the Art Quantification &
Representation
Quantification Representation
Bull’s eye plot in
Contiguous short-axis slices 17-segment
model
basal
mid-cavity
apical
Slice thickness
apex
19. Material
Pre-processing
Infarct segmentation
Methodology Infarct core segmentation
Peri-infarct segmentation
No-reflow segmentation
Quantification
Representations
Material
Population study
20 patients
Ages: 53 +10 years
Acute MI (<2 weeks
after heart attack)
MRI Protocol
3 T MR Magnet
PSIR sequence
26. Material
Pre-processing
Infarct segmentation
Methodology
Infarct core segmentation
Peri-infarct segmentation
No-reflow segmentation
Quantification
Representations
Output:
Infarct segmentation Hyper-enhanced (HE) region
Input: Gaussian mixture model (GMM)
Pre-processed myocardium
Gaussian parameters
Mixture of Gaussian distribution:
Estimate θ by iterative M-step:
expectation-maximization
(EM) algorithm
Gaussian distribution:
E-step:
Voxel intensity
27. Material
Pre-processing
Infarct segmentation
Methodology
Infarct core segmentation
Peri-infarct segmentation
No-reflow segmentation
Quantification
Representations
Infarct segmentation
Two-way 3D connectivity analysis
False positive compensation noisy acquisition, blood pool
artifact, or partial volume effect (PVE)
Detected infarct continuous in 3D image stack
28. Material
Pre-processing
Infarct segmentation
Methodology
Infarct core segmentation
Peri-infarct segmentation
No-reflow segmentation
Quantification
Representations
Infarct segmentation
Two-way 3D connectivity analysis
False positive compensation noisy acquisition, blood pool
artifact, or partial volume effect (PVE)
Detected infarct continuous in 3D image stack
Feature analysis
• Minimum size
• Sub-endocardial distance
• Solidity
29. Material
Pre-processing
Infarct segmentation
Methodology
Infarct core segmentation
Peri-infarct segmentation
No-reflow segmentation
Quantification
Representations
Infarct core segmentation
FWHM Thresholding
Feature analysis
Inclusion of no-reflow area
Morphological filling and closing with endocardium
3D connectivity analysis
Minimum size
30. Material
Pre-processing
Infarct segmentation
Methodology
Infarct core segmentation
Peri-infarct segmentation
No-reflow segmentation
Quantification
Representations
Infarct core segmentation
FWHM Thresholding
Feature analysis
Inclusion of no-reflow area
Morphological filling and closing with endocardium
3D connectivity analysis
Minimum size
31. Material
Pre-processing
Infarct segmentation
Methodology
Infarct core segmentation
Peri-infarct segmentation
No-reflow segmentation
Quantification
Representations
Peri-infarct segmentation
Spatial-weighted Fuzzy clustering Optimization of objective function
O for the optimal cluster center c
Input and degree of membership u:
Cluster:
K = 2 normal and peri-infarct
Spatial-weighted fuzzy membership:
Euclidean distance of myocardium
pixels to the infarct core
32. Material
Pre-processing
Infarct segmentation
Methodology
Infarct core segmentation
Peri-infarct segmentation
No-reflow segmentation
Quantification
Representations
Peri-infarct segmentation
Spatial-weighted Fuzzy clustering Optimization of objective function
O for the optimal cluster center c
Input and degree of membership u:
Cluster:
K = 2 normal and peri-infarct
Spatial-weighted fuzzy membership:
Spatial weight pik
Euclidean distance of myocardium
pixels to the infarct core
33. Material
Pre-processing
Infarct segmentation
Methodology
Infarct core segmentation
Peri-infarct segmentation
No-reflow segmentation
Quantification
Representations
Peri-infarct segmentation
Spatial-weighted Fuzzy clustering Optimization of objective function
O for the optimal cluster center c
Input and degree of membership u:
Cluster:
K = 2 normal and peri-infarct
Spatial-weighted fuzzy membership:
Output Spatial weight pik
Defuzzification
Euclidean distance of myocardium
pixels to the infarct core
34. Material
Pre-processing
Infarct segmentation
Methodology
Infarct core segmentation
Peri-infarct segmentation
No-reflow segmentation
Quantification
Representations
No-reflow segmentation
1 Dark region
surrounded by
infarct core
Input: Infarct core
35. Material
Pre-processing
Infarct segmentation
Methodology
Infarct core segmentation
Peri-infarct segmentation
No-reflow segmentation
Quantification
Representations
No-reflow segmentation
1 Dark region
surrounded by
infarct core
Input: Infarct core 2 Adjacent to endocardium
36. Material
Pre-processing
Infarct segmentation
Methodology
Infarct core segmentation
Peri-infarct segmentation
No-reflow segmentation
Quantification
Representations
No-reflow segmentation
1 Dark region
surrounded by
infarct core
Input: Infarct core 2 Adjacent to endocardium
3 Extent of MO ≠transmural
Spatial constraint
Dist myo : normalized relative
distance of myocardium pixels to
endocardium
Limit for no-reflow region:
37. Material
Pre-processing
Infarct segmentation
Methodology
Infarct core segmentation
Peri-infarct segmentation
No-reflow segmentation
Quantification
Representations
No-reflow segmentation
1 Dark region
surrounded by
infarct core
Input: Infarct core 2 Adjacent to endocardium
3 Extent of MO ≠transmural
Spatial constraint
Dist myo : normalized relative
distance of myocardium pixels to
endocardium
Limit for no-reflow region:
Output
38. Material
Pre-processing
Infarct segmentation
Methodology
Infarct core segmentation
Peri-infarct segmentation
No-reflow segmentation
Quantification
Representations
Quantification
Area in
39. Material
Pre-processing
Infarct segmentation
Methodology
Infarct core segmentation
Peri-infarct segmentation
No-reflow segmentation
Quantification
Representations
Quantification
Area in
Volumetric quantification for the whole myocardium
where,
i = the current slice from N image slices in MRI stack. Volume in
Also, in % of myocardium
40. Material
Pre-processing
Infarct segmentation
Methodology
Infarct core segmentation
Peri-infarct segmentation
No-reflow segmentation
Quantification
Representations
Representation
Quantitative
Q Bull’s eye plot in 16-segment model
u Infarct
Core
a
l
Peri-
i
infarct
t
a
t No-
i reflow
v
e All
41. GUI Implementation
Evaluation of Infarct Size
Quantification
Results Evaluation of the Infarct
Area Segmentation
Visual Evaluation of HIA
Segmentation
Navigation Manual contouring
Menu toolbar
Status GUI
Image display
Patient Display
information Analysis panel segmentation Display quantification
42. GUI Implementation
Evaluation of Infarct Size
Quantification
Results Evaluation of the Infarct
Area Segmentation
Visual Evaluation of HIA
Segmentation
Navigation Manual contouring
Menu toolbar
Status GUI
Image display
Patient Display
information Analysis panel segmentation Display quantification
43. GUI Implementation
Evaluation of Infarct Size
Quantification
Results Evaluation of the Infarct
Area Segmentation
Visual Evaluation of HIA
Segmentation
Navigation Manual contouring
Menu toolbar
Status GUI
Image display
Patient Display
information Analysis panel segmentation Display quantification
44. GUI Implementation
Evaluation of Infarct Size
Quantification
Results Evaluation of the Infarct
Area Segmentation
Visual Evaluation of HIA
Segmentation
Navigation Manual contouring
Menu toolbar
Status GUI
Image display
Patient Display
information Analysis panel segmentation Display quantification
45. GUI Implementation
Evaluation of Infarct Size
Quantification
Results Evaluation of the Infarct
Area Segmentation
Visual Evaluation of HIA
Segmentation
Navigation Manual contouring
Menu toolbar
Status GUI
Image display
Patient Display
information Analysis panel segmentation Display quantification
46. GUI Implementation
Evaluation of Infarct Size
Quantification
Results Evaluation of the Infarct
Area Segmentation
Visual Evaluation of HIA
Segmentation
Navigation Manual contouring
Menu toolbar
Status GUI
Image display
Patient Display
information Analysis panel segmentation Display quantification
47. GUI Implementation
Evaluation of Infarct Size
Quantification
Results Evaluation of the Infarct
Area Segmentation
Visual Evaluation of HIA
Segmentation
Navigation Manual contouring
Menu toolbar
Status GUI
Image display
Patient Display
information Analysis panel segmentation Display quantification
48. GUI Implementation
Evaluation of Infarct Size
Quantification
Results Evaluation of the Infarct
Area Segmentation
Visual Evaluation of HIA
Segmentation
Evaluation of Infarct Size Quantification
Comparison of our automatic method & ground truth (manual total-infarct tracing from 2 observers)
Vo l u m e
mean of the differences = 2.78 cm3
SD of the differences = 3.27 cm3
49. GUI Implementation
Evaluation of Infarct Size
Quantification
Results Evaluation of the Infarct
Area Segmentation
Visual Evaluation of HIA
Segmentation
Evaluation of Infarct Size Quantification
Area
mean of the differences = 0.45 cm2
SD of the differences = 0.75 cm2
50. GUI Implementation
Evaluation of Infarct Size
Quantification
Results Evaluation of the Infarct
Area Segmentation
Visual Evaluation of HIA
Segmentation
Evaluation of the Infarct Segmentation
By Kappa coefficient, between the automatic segmentation results and ground-truths
(manual total-infarct tracing from 2 observers)
Kappa statistics for infarct-segmentation comparison of automatic methods and observers agreement
51. GUI Implementation
Evaluation of Infarct Size
Quantification
Results Evaluation of the Infarct
Area Segmentation
Visual Evaluation of HIA
Segmentation
Visual Evaluation of HIA Segmentation
Rating score for subjective-visual evaluation of HIA segmentation
Visual evaluation result for HIA segmentation
from 116 images from 20 patients
52. GUI Implementation
Evaluation of Infarct Size
Quantification
Results Evaluation of the Infarct
Area Segmentation
Visual Evaluation of HIA
Segmentation
Visual Evaluation of HIA Segmentation
Issues in HIA segmentation (errors are indicated with white arrow)
Error in the infarct Error in the peri-infact Error in the no-reflow Unconnected infarct
core segmentation segmentation segmentation regions
53. GUI Implementation
Evaluation of Infarct Size
Quantification
Results Evaluation of the Infarct
Area Segmentation
Visual Evaluation of HIA
Segmentation
Visual Evaluation of HIA Segmentation
Example of correct infarct segmentation results with our method
Robustness of the infarct segmentation method Image with narrow range
neighboring slices by using two-way 3D connectivity of signal intensity
54. Conclusions
1 A fully automatic system for myocardial infarction quantification has been
implemented
2 The automatic quantification and segmentation results had been
evaluated and gave the best performance with fast computational time
Improvements made in this thesis work:
Clustering method used rather than strict threshold determination
Elimination of false positive cases were tackled
The definitions for peri-infarct and no-reflow segmentation give promising result
55. Conclusions
1 A fully automatic system for myocardial infarction quantification has been
implemented
2 The automatic quantification and segmentation results had been
evaluated and gave the best performance with fast computational time
Improvements made in this thesis work:
Clustering method used rather than strict threshold determination
Elimination of false positive cases were tackled
The definitions for peri-infarct and no-reflow segmentation give promising result
Future works
Validation with histopathology
Extend the representation in 3D model
Implementation in C++
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62. Infarct Segmentation
Comparison
2 SD FWHM Combined threshold-
Feature Analysis
Proposed method Ground truth 1 Ground truth 2
63. HIA Segmentation
Comparison
Yan et al. Schmidt et al. Hundely et al. Proposed
(2006) (2007) (2010) method
72. Infarct segmentation
Characteristics:
• The distribution of myocardium SI according to Gaussian
• Infarct always starts from endocardial
• Infarct regions are compact-shape of certain size
A = area of the region
H = the convex hull area of the polygon
approximating the region shape
Let me begin with the introduction of … What is MI?Myocardial Infarction (MI), is commonly known as heart attack. Myocardial came from the termmyocardium means heart muscle, “Infarction” refers to the tissue death caused by the blocking of the coronary artery by the plaque that will prevent supplies of blood and oxygen. Based on medical studies, infarct area may contain heterogeneous zones, we define as HIA, which may have different recovery. These areas consist of infarct core isfully infarcted tissueperi-infarct, or the border zone;is a mixture of healthy and diseased myocardium. And microvascular obstruction (or calledno-reflow phenomena) is area with permanent absence of tissue perfusion.
Particularly, DE-MRI can be considered as the gold standard for the assessment of myocardial viability.The name is delay-enhancement MRI because the images are taken on around 10 – 15 minutes after the gadolonium-based contrast agent injection. The acquisition of these MRI slices is consecutively performed from basal to apex part of the heart. An example of a DE-MR image is shown here.. This is myocardium which surrounds the LV, with the myocardium border consists of endocardium and epicardium. Infarct region is indicated with this hyper-enhanced area.
Particularly, DE-MRI can be considered as the gold standard for the assessment of myocardial viability.The name is delay-enhancement MRI because the images are taken on around 10 – 15 minutes after the gadolonium-based contrast agent injection. The acquisition of these MRI slices is consecutively performed from basal to apex part of the heart. An example of a DE-MR image is shown here.. This is myocardium which surrounds the LV, with the myocardium border consists of endocardium and epicardium. Infarct region is indicated with this hyper-enhanced area.
Particularly, DE-MRI can be considered as the gold standard for the assessment of myocardial viability.The name is delay-enhancement MRI because the images are taken on around 10 – 15 minutes after the gadolonium-based contrast agent injection. The acquisition of these MRI slices is consecutively performed from basal to apex part of the heart. An example of a DE-MR image is shown here.. This is myocardium which surrounds the LV, with the myocardium border consists of endocardium and epicardium. Infarct region is indicated with this hyper-enhanced area.
Particularly, DE-MRI can be considered as the gold standard for the assessment of myocardial viability.The name is delay-enhancement MRI because the images are taken on around 10 – 15 minutes after the gadolonium-based contrast agent injection. The acquisition of these MRI slices is consecutively performed from basal to apex part of the heart. An example of a DE-MR image is shown here.. This is myocardium which surrounds the LV, with the myocardium border consists of endocardium and epicardium. Infarct region is indicated with this hyper-enhanced area.
Currently, the main interest for the cardiologists is this HIA which must be clearly defined to validate its correlation to the patient’s arrhythmia and mortality after heart attack.However, problem arose since HIA is hard to be distinguished accurately by human eye. Moreover, no automatic solution available.As a consequence, an automatic quantification of infarct is becoming very important . Thus, our project goals are to:Develop automatic segmentation and quantification methods, by…AndImplement the method into a clinical software to help the cardiologistssince doing the manual segmentation is time consuming and sometimes impossibleIn order to be able to solve these problem, a bibliographic survey was done and reviewed in the following slides
Currently, the main interest for the cardiologists is this HIA which must be clearly defined to validate its correlation to the patient’s arrhythmia and mortality after heart attack.However, problem arose since HIA is hard to be distinguished accurately by human eye. Moreover, no automatic solution available.As a consequence, an automatic quantification of infarct is becoming very important . Thus, our project goals are to:Develop automatic segmentation and quantification methods, by…AndImplement the method into a clinical software to help the cardiologistssince doing the manual segmentation is time consuming and sometimes impossibleIn order to be able to solve these problem, a bibliographic survey was done and reviewed in the following slides
There are several approaches for determining the infarct area.First approach is standard deviation based intensity thresholding, which is the most popular method. Commonly, threshold for infarct is 2 SD above the mean of the normal myocardium. Full width half maximum threshold then was proposed as the half of the maximum signal intensity inside the infarct region. But, these two methods are lack of spatial coherence. Thus, a more sophisticated results were obtained by combining Standard deviation and FWHM-based thresholding with regional feature analysis. Apart from simple intensity thresholding, other methods implement watershed and clustering technique such as k-means, or mixture model and fuzzy membership.
There are several approaches for determining the infarct area.First approach is standard deviation based intensity thresholding, which is the most popular method. Commonly, threshold for infarct is 2 SD above the mean of the normal myocardium. Full width half maximum threshold then was proposed as the half of the maximum signal intensity inside the infarct region. But, these two methods are lack of spatial coherence. Thus, a more sophisticated results were obtained by combining Standard deviation and FWHM-based thresholding with regional feature analysis. Apart from simple intensity thresholding, other methods implement watershed and clustering technique such as k-means, or mixture model and fuzzy membership.
There are several approaches for determining the infarct area.First approach is standard deviation based intensity thresholding, which is the most popular method. Commonly, threshold for infarct is 2 SD above the mean of the normal myocardium. Full width half maximum threshold then was proposed as the half of the maximum signal intensity inside the infarct region. But, these two methods are lack of spatial coherence. Thus, a more sophisticated results were obtained by combining Standard deviation and FWHM-based thresholding with regional feature analysis. Apart from simple intensity thresholding, other methods implement watershed and clustering technique such as k-means, or mixture model and fuzzy membership.
There are several approaches for determining the infarct area.First approach is standard deviation based intensity thresholding, which is the most popular method. Commonly, threshold for infarct is 2 SD above the mean of the normal myocardium. Full width half maximum threshold then was proposed as the half of the maximum signal intensity inside the infarct region. But, these two methods are lack of spatial coherence. Thus, a more sophisticated results were obtained by combining Standard deviation and FWHM-based thresholding with regional feature analysis. Apart from simple intensity thresholding, other methods implement watershed and clustering technique such as k-means, or mixture model and fuzzy membership.
There are several approaches for determining the infarct area.First approach is standard deviation based intensity thresholding, which is the most popular method. Commonly, threshold for infarct is 2 SD above the mean of the normal myocardium. Full width half maximum threshold then was proposed as the half of the maximum signal intensity inside the infarct region. But, these two methods are lack of spatial coherence. Thus, a more sophisticated results were obtained by combining Standard deviation and FWHM-based thresholding with regional feature analysis. Apart from simple intensity thresholding, other methods implement watershed and clustering technique such as k-means, or mixture model and fuzzy membership.
Concerning the HIA segmentation, since peri-infarct consist of healthy and infarcted tissues, the appearance is gray, As shown in this image, infarct core is red whereas peri-infarct is in yelow.Yet, only a few papers have tackled segmentation of HIA and all of them are only based on simple intensity thresholding. According to the clinical validation, Schmidt method based on FWHM criteria gave the best definition.Meanwhile, no-reflow area is very severe infarct, defined as black region within the infarct core. But, from all reviews, none of them had tried to segment or quantify this region so that there is no exact threshold definition.
Concerning the HIA segmentation, since peri-infarct consist of healthy and infarcted tissues, the appearance is gray, As shown in this image, infarct core is red whereas peri-infarct is in yelow.Yet, only a few papers have tackled segmentation of HIA and all of them are only based on simple intensity thresholding. According to the clinical validation, Schmidt method based on FWHM criteria gave the best definition.Meanwhile, no-reflow area is very severe infarct, defined as black region within the infarct core. But, from all reviews, none of them had tried to segment or quantify this region so that there is no exact threshold definition.
Concerning the HIA segmentation, since peri-infarct consist of healthy and infarcted tissues, the appearance is gray, As shown in this image, infarct core is red whereas peri-infarct is in yelow.Yet, only a few papers have tackled segmentation of HIA and all of them are only based on simple intensity thresholding. According to the clinical validation, Schmidt method based on FWHM criteria gave the best definition.Meanwhile, no-reflow area is very severe infarct, defined as black region within the infarct core. But, from all reviews, none of them had tried to segment or quantify this region so that there is no exact threshold definition.
Regarding the quantification.The most common method for volumetric quantification is contiguous short-axis model, which is the sum of corresponding area on all contiguous slices and multiplied by the slice thickness. Then, for representation, it is strongly recommended to use bull's eye plot with this standard 17 segment model. Before, the left ventricle must be divided into equal thirds in the long axis view to generates basal, mid-cavity, and apical sections. Then, basal and mid-cavity are divided into six segments, whereas apical part only have four segments. This figure shows the 17-segment model with each color is associated with the perfusion of a specific coronary…So far, we’ve seen the reviews of state-of-the art In the next slide, I’ll explain the solution wit our proposed method..
Regarding the quantification.The most common method for volumetric quantification is contiguous short-axis model, which is the sum of corresponding area on all contiguous slices and multiplied by the slice thickness. Then, for representation, it is strongly recommended to use bull's eye plot with this standard 17 segment model. Before, the left ventricle must be divided into equal thirds to generates basal, mid-cavity, and apical sections. Then, basal and mid-cavity are divided into six segments, whereas apical part only have four segments. This figure shows the 17-segment model with each color is associated with the perfusion of a specific coronary…So far, we’ve seen the reviews of state-of-the art In the next slide, I’ll explain the solution wit our proposed method..
In our study, images were acquired from 20 patients with acute MI who conducted MRI examination less than two weeks after experienced heart attack. The patients were examined with 3T MRI and we used the phase images from PSIR sequenceHere is the block diagram of the complete process Since it is automatic, the only input required from the user is the correct myocardium borders; means epicardium and endocardium.Then, images are ready to be pre-processed before the infarct segmentation. After, HIA segmentation is performed and the results will be used for quantification and representation
The first step is pre-processing..Initially, we increased the image resolution by bicubic interpolation to reduce the risk for inaccurate quantification. Then, contrast enhancement was performed to make the normal myocardium as dark as possible but keep the infarct area bright.besides ,position of a patient’s breath-hold is not the same from one acquisition to another, leading to displacement of heart location between image slices. Hence, rigid registration to the center of myocardium must be performed to ensure that myocardium propagates correctly through entire 3D image stack. Then, we obtained the aligned image slices..After that, we applied image filtering to smooth the histogram space.
The first step is pre-processing..Initially, we increased the image resolution by bicubic interpolation to reduce the risk for inaccurate quantification. Then, contrast enhancement was performed to make the normal myocardium as dark as possible but keep the infarct area bright.besides ,position of a patient’s breath-hold is not the same from one acquisition to another, leading to displacement of heart location between image slices. Hence, rigid registration to the center of myocardium must be performed to ensure that myocardium propagates correctly through entire 3D image stack. Then, we obtained the aligned image slices..After that, we applied image filtering to smooth the histogram space.
The first step is pre-processing..Initially, we increased the image resolution by bicubic interpolation to reduce the risk for inaccurate quantification. Then, contrast enhancement was performed to make the normal myocardium as dark as possible but keep the infarct area bright.besides ,position of a patient’s breath-hold is not the same from one acquisition to another, leading to displacement of heart location between image slices. Hence, rigid registration to the center of myocardium must be performed to ensure that myocardium propagates correctly through entire 3D image stack. Then, we obtained the aligned image slices..After that, we applied image filtering to smooth the histogram space.
The first step is pre-processing..Initially, we increased the image resolution by bicubic interpolation to reduce the risk for inaccurate quantification. Then, contrast enhancement was performed to make the normal myocardium as dark as possible but keep the infarct area bright.besides ,position of a patient’s breath-hold is not the same from one acquisition to another, leading to displacement of heart location between image slices. Hence, rigid registration to the center of myocardium must be performed to ensure that myocardium propagates correctly through entire 3D image stack. Then, we obtained the aligned image slices..After that, we applied image filtering to smooth the histogram space.
Pre-processed myocardium is the input for infarct segmentation stage. Based on our findings, the distribution shape of myocardium is bimodal histogram with two Gaussian. Hence, we applied a Gaussian mixture model to separate between normal and infarct region. Myocardium intensity can be modeled by weighted Gaussian distributions, given the voxel intensity values x, The unknown Gaussian mixture parameters (means, variances and proportion) are tuned using an iterative expectation maximization EM algorithm. In E-step, the responsibility is calculated to be used to estimate new means, covariance and weights in the M-step, iteratively. When it is converged, we stop the and the optimal hyper-enhanced threshold is the average of these means…The output for this step is region having intensities above this threshold.
Pre-processed myocardium is the input for infarct segmentation stage. Based on our findings, the distribution shape of myocardium is bimodal histogram with two Gaussian. Hence, we applied a Gaussian mixture model to separate between normal and infarct region. Myocardium intensity can be modeled by weighted Gaussian distributions, given the voxel intensity values x, The unknown Gaussian mixture parameters (means, variances and proportion) are tuned using an iterative expectation maximization EM algorithm. In E-step, the responsibility is calculated to be used to estimate new means, covariance and weights in the M-step, iteratively. When it is converged, we stop the and the optimal hyper-enhanced threshold is the average of these means…The output for this step is region having intensities above this threshold.
Pre-processed myocardium is the input for infarct segmentation stage. Based on our findings, the distribution shape of myocardium is bimodal histogram with two Gaussian. Hence, we applied a Gaussian mixture model to separate between normal and infarct region. Myocardium intensity can be modeled by weighted Gaussian distributions, given the voxel intensity values x, The unknown Gaussian mixture parameters (means, variances and proportion) are tuned using an iterative expectation maximization EM algorithm. In E-step, the responsibility is calculated to be used to estimate new means, covariance and weights in the M-step, iteratively. When it is converged, we stop the and the optimal hyper-enhanced threshold is the average of these means…The output for this step is region having intensities above this threshold.
To refine the result, we must perform the following steps..First is the 2-way 3D connectivity analysis for false positive compensation due to noisy acquisition, blood pool artifact, or PVE.. PVE is a case when a single voxel contains a mixture of multiple tissues, which can lead to a segmentation error. As our general assumption is that the infarct region must be continuous in 3D image stacks, we applied Forward connectivity from basal to apical, followed by reverse connectivity to remove unconnected region to neighboring slices.Then, in feature analysis, we remove any bright regions less than a minimum sizeYet, if the distance of a region is far from endocardial, it is discarded.Lastly, solidity must be checked because the infarct region is compact.Output for infarct region is shown here…
To refine the result, we must perform the following steps..First is the 2-way 3D connectivity analysis for false positive compensation due to noisy acquisition, blood pool artifact, or PVE.. PVE is a case when a single voxel contains a mixture of multiple tissues, which can lead to a segmentation error. As our general assumption is that the infarct region must be continuous in 3D image stacks, we applied Forward connectivity from basal to apical, followed by reverse connectivity to remove unconnected region to neighboring slices.Then, in feature analysis, we remove any bright regions less than a minimum sizeYet, if the distance of a region is far from endocardial, it is discarded.Lastly, solidity must be checked because the infarct region is compact.Output for infarct region is shown here…
The result of infarct segmentation in the previous step is the input for the infarct core segmentation. Based on the consensus of the doctors in the hospital, we defined the core-infarct threshold with FWHM criterion.After, the no-reflow area is included by morphological filling and closing operation with a bar shape kernel in four orientations. Moreover, we performed the connectivity analysis and set the minimum size of infarct core.
The result of infarct segmentation in the previous step is the input for the infarct core segmentation. Based on the consensus of the doctors in the hospital, we defined the core-infarct threshold with FWHM criterion.After, the no-reflow area is included by morphological filling and closing operation with a bar shape kernel in four orientations. Moreover, we performed the connectivity analysis and set the minimum size of infarct core.
After infarct core region is obtained, we solve the segmentation for peri-infarct. Given that the intensity of peri-infarct is between the normal tissue and infarct core, and the location is surrounding the infarct core;fuzzy membership associated with spatial and intensity is the best to define peri-infarct, as it may be regarded as 'fuzzy' area. For this reason, we applied the spatial-weighted fuzzy clustering.So, the Input to this step is the myocardium pixels having the intensities above the lower mean and below infarct core threshold.Then, optimization of an objective function must be solved for the optimal cluster center and degree of membership. By incorporating the spatial information, this fuzzy membership u*ik is can be extended as:where pik is the spatial weight, calculated as the Euq. distance of myocardium pixels to the infarct core regions Consequently, a pixel gets higher membership degree when it has a high signal intensity uik and close to infarct core pik.Once this clustering algorithm has converged, a defuzzication process was carried on to obtain the segmentation like this.
After infarct core region is obtained, we solve the segmentation for peri-infarct. Given that the intensity of peri-infarct is between the normal tissue and infarct core, and the location is surrounding the infarct core;fuzzy membership associated with spatial and intensity is the best to define peri-infarct, as it may be regarded as 'fuzzy' area. For this reason, we applied the spatial-weighted fuzzy clustering.So, the Input to this step is the myocardium pixels having the intensities above the lower mean and below infarct core threshold.Then, optimization of an objective function must be solved for the optimal cluster center and degree of membership. By incorporating the spatial information, this fuzzy membership u*ik is can be extended as:where pik is the spatial weight, calculated as the Euq. distance of myocardium pixels to the infarct core regions Consequently, a pixel gets higher membership degree when it has a high signal intensity uik and close to infarct core pik.Once this clustering algorithm has converged, a defuzzication process was carried on to obtain the segmentation like this.
After infarct core region is obtained, we solve the segmentation for peri-infarct. Given that the intensity of peri-infarct is between the normal tissue and infarct core, and the location is surrounding the infarct core;fuzzy membership associated with spatial and intensity is the best to define peri-infarct, as it may be regarded as 'fuzzy' area. For this reason, we applied the spatial-weighted fuzzy clustering.So, the Input to this step is the myocardium pixels having the intensities above the lower mean and below infarct core threshold.Then, optimization of an objective function must be solved for the optimal cluster center and degree of membership. By incorporating the spatial information, this fuzzy membership u*ik is can be extended as:where pik is the spatial weight, calculated as the Euq. distance of myocardium pixels to the infarct core regions Consequently, a pixel gets higher membership degree when it has a high signal intensity uik and close to infarct core pik.Once this clustering algorithm has converged, a defuzzication process was carried on to obtain the segmentation like this.
The last infarct zone to be segmented is the no-reflow area. Input for this step is the infarct core region.There are 3 assumptions applied for this segmentation. First, since no-reflow is dark pixels inside the infarct core, which may have the same intensity as normal regions. No-reflow region is defined as region having intensity lower then this HE threshold.Second, no-reflow region must be adjacent to the endocardiumThird, to limit the allowed extent of no-reflow region, we build a spatial constraint, calculated as the normalized relative distance of myocardium pixels to endo., taken in each angle of radial chord.. Thus, we set the limit to be less than 0.5 since no-reflow is rarely extent near to epicardium. And this is the output..
The last infarct zone to be segmented is the no-reflow area. Input for this step is the infarct core region.There are 3 assumptions applied for this segmentation. First, since no-reflow is dark pixels inside the infarct core, which may have the same intensity as normal regions. No-reflow region is defined as region having intensity lower then this HE threshold.Second, no-reflow region must be adjacent to the endocardiumThird, to limit the allowed extent of no-reflow region, we build a spatial constraint, calculated as the normalized relative distance of myocardium pixels to endo., taken in each angle of radial chord.. Thus, we set the limit to be less than 0.5 since no-reflow is rarely extent near to epicardium. And this is the output..
The last infarct zone to be segmented is the no-reflow area. Input for this step is the infarct core region.There are 3 assumptions applied for this segmentation. First, since no-reflow is dark pixels inside the infarct core, which may have the same intensity as normal regions. No-reflow region is defined as region having intensity lower then this HE threshold.Second, no-reflow region must be adjacent to the endocardiumThird, to limit the allowed extent of no-reflow region, we build a spatial constraint, calculated as the normalized relative distance of myocardium pixels to endo., taken in each angle of radial chord.. Thus, we set the limit to be less than 0.5 since no-reflow is rarely extent near to epicardium. And this is the output..
The last infarct zone to be segmented is the no-reflow area. Input for this step is the infarct core region.There are 3 assumptions applied for this segmentation. First, since no-reflow is dark pixels inside the infarct core, which may have the same intensity as normal regions. No-reflow region is defined as region having intensity lower then this HE threshold.Second, no-reflow region must be adjacent to the endocardiumThird, to limit the allowed extent of no-reflow region, we build a spatial constraint, calculated as the normalized relative distance of myocardium pixels to endo., taken in each angle of radial chord.. Thus, we set the limit to be less than 0.5 since no-reflow is rarely extent near to epicardium. And this is the output..
subsequently, segmentation results then are used for quantification step. First we did quantification of areas in each slice…Quantified area in true values is the multiplication of area in pixels by the pixel spacing obtained from DICOM information. Since no-reflow is included in infarct core, total infarct area is calculated as core plus peri-infarct.The volume for the whole myocardium is the summation of the area multiplied by d which is the difference between consecutive slice.The volume is expressed in mm3 and as a percentage of the total myocardium, to know how severe and heterogenic the infarct is
subsequently, segmentation results then are used for quantification step. First we did quantification of areas in each slice…Quantified area in true values is the multiplication of area in pixels by the pixel spacing obtained from DICOM information. Since no-reflow is included in infarct core, total infarct area is calculated as core plus peri-infarct.The volume for the whole myocardium is the summation of the area multiplied by d which is the difference between consecutive slice.The volume is expressed in mm3 and as a percentage of the total myocardium, to know how severe and heterogenic the infarct is
Afterwards, to see the result qualitatively, the segmented infarct regions are represented visually in Label and contour. We use different color for each; red for core-infarct, yellow for peri-infarct and green for no-reflow region.For quantitative representation, Bull’s eye plot is used. Unfortunately in the real data, apex slice (17th segment) was not available, so that we implement 16-segment model. In this model, sector is color coded according to its severity, which depends on the level of infarct percentage. Exact percentage values are also indicated in each sector.Now, that we’ve discussed the proposed solution, it is time to seeour clinical software in MATLAB GUI
I will explain the function of each panel..Menu toolbar, to browse for patient folder (F) and choose the slices, load the saved data (L), save, zoom, n reset the GUI (R)2. Display the current processed image3. Patient information panel4. Navigation to the next/previous slice.5. Status GUI to display the ongoing process6. Manual contouring made by user for endocardium, epicardium, and manual infarct. The user can trace point-by-point the contour and the confirmed drawing will be displayed as blue borders.7. Analysis panel to perform the automatic segmentation for all infarct zones, to assist the semi-automatic segmentation when the user is not satisfied with the automatic result so that the threshold can be specified manually in the myocardium histogram..next is to calculate the quantification parameters, and to represent the global infarct analysis in Bull's eye view.8. Display automatic infarct segmentation results in label or contour. 9. Display all quantification values in area and volumeWhen all of the process have been performed, the user can press 'P' to generate a report for the patient in Excel file containing the patient info, quantification values and the Bull's eye representation.
I will explain the function of each panel..Menu toolbar, to browse for patient folder (F) and choose the slices, load the saved data (L), save, zoom, n reset the GUI (R)2. Display the current processed image3. Patient information panel4. Navigation to the next/previous slice.5. Status GUI to display the ongoing process6. Manual contouring made by user for endocardium, epicardium, and manual infarct. The user can trace point-by-point the contour and the confirmed drawing will be displayed as blue borders.7. Analysis panel to perform the automatic segmentation for all infarct zones, to assist the semi-automatic segmentation when the user is not satisfied with the automatic result so that the threshold can be specified manually in the myocardium histogram..next is to calculate the quantification parameters, and to represent the global infarct analysis in Bull's eye view.8. Display automatic infarct segmentation results in label or contour. 9. Display all quantification values in area and volumeWhen all of the process have been performed, the user can press 'P' to generate a report for the patient in Excel file containing the patient info, quantification values and the Bull's eye representation.
I will explain the function of each panel..Menu toolbar, to browse for patient folder (F) and choose the slices, load the saved data (L), save, zoom, n reset the GUI (R)2. Display the current processed image3. Patient information panel4. Navigation to the next/previous slice.5. Status GUI to display the ongoing process6. Manual contouring made by user for endocardium, epicardium, and manual infarct. The user can trace point-by-point the contour and the confirmed drawing will be displayed as blue borders.7. Analysis panel to perform the automatic segmentation for all infarct zones, to assist the semi-automatic segmentation when the user is not satisfied with the automatic result so that the threshold can be specified manually in the myocardium histogram..next is to calculate the quantification parameters, and to represent the global infarct analysis in Bull's eye view.8. Display automatic infarct segmentation results in label or contour. 9. Display all quantification values in area and volumeWhen all of the process have been performed, the user can press 'P' to generate a report for the patient in Excel file containing the patient info, quantification values and the Bull's eye representation.
I will explain the function of each panel..Menu toolbar, to browse for patient folder (F) and choose the slices, load the saved data (L), save, zoom, n reset the GUI (R)2. Display the current processed image3. Patient information panel4. Navigation to the next/previous slice.5. Status GUI to display the ongoing process6. Manual contouring made by user for endocardium, epicardium, and manual infarct. The user can trace point-by-point the contour and the confirmed drawing will be displayed as blue borders.7. Analysis panel to perform the automatic segmentation for all infarct zones, to assist the semi-automatic segmentation when the user is not satisfied with the automatic result so that the threshold can be specified manually in the myocardium histogram..next is to calculate the quantification parameters, and to represent the global infarct analysis in Bull's eye view.8. Display automatic infarct segmentation results in label or contour. 9. Display all quantification values in area and volumeWhen all of the process have been performed, the user can press 'P' to generate a report for the patient in Excel file containing the patient info, quantification values and the Bull's eye representation.
I will explain the function of each panel..Menu toolbar, to browse for patient folder (F) and choose the slices, load the saved data (L), save, zoom, n reset the GUI (R)2. Display the current processed image3. Patient information panel4. Navigation to the next/previous slice.5. Status GUI to display the ongoing process6. Manual contouring made by user for endocardium, epicardium, and manual infarct. The user can trace point-by-point the contour and the confirmed drawing will be displayed as blue borders.7. Analysis panel to perform the automatic segmentation for all infarct zones, to assist the semi-automatic segmentation when the user is not satisfied with the automatic result so that the threshold can be specified manually in the myocardium histogram..next is to calculate the quantification parameters, and to represent the global infarct analysis in Bull's eye view.8. Display automatic infarct segmentation results in label or contour. 9. Display all quantification values in area and volumeWhen all of the process have been performed, the user can press 'P' to generate a report for the patient in Excel file containing the patient info, quantification values and the Bull's eye representation.
I will explain the function of each panel..Menu toolbar, to browse for patient folder (F) and choose the slices, load the saved data (L), save, zoom, n reset the GUI (R)2. Display the current processed image3. Patient information panel4. Navigation to the next/previous slice.5. Status GUI to display the ongoing process6. Manual contouring made by user for endocardium, epicardium, and manual infarct. The user can trace point-by-point the contour and the confirmed drawing will be displayed as blue borders.7. Analysis panel to perform the automatic segmentation for all infarct zones, to assist the semi-automatic segmentation when the user is not satisfied with the automatic result so that the threshold can be specified manually in the myocardium histogram..next is to calculate the quantification parameters, and to represent the global infarct analysis in Bull's eye view.8. Display automatic infarct segmentation results in label or contour. 9. Display all quantification values in area and volumeWhen all of the process have been performed, the user can press 'P' to generate a report for the patient in Excel file containing the patient info, quantification values and the Bull's eye representation.
I will explain the function of each panel..Menu toolbar, to browse for patient folder (F) and choose the slices, load the saved data (L), save, zoom, n reset the GUI (R)2. Display the current processed image3. Patient information panel4. Navigation to the next/previous slice.5. Status GUI to display the ongoing process6. Manual contouring made by user for endocardium, epicardium, and manual infarct. The user can trace point-by-point the contour and the confirmed drawing will be displayed as blue borders.7. Analysis panel to perform the automatic segmentation for all infarct zones, to assist the semi-automatic segmentation when the user is not satisfied with the automatic result so that the threshold can be specified manually in the myocardium histogram..next is to calculate the quantification parameters, and to represent the global infarct analysis in Bull's eye view.8. Display automatic infarct segmentation results in label or contour. 9. Display all quantification values in area and volumeWhen all of the process have been performed, the user can press 'P' to generate a report for the patient in Excel file containing the patient info, quantification values and the Bull's eye representation.
In order to evaluate the performance of our method, the results of infarct quantification will be compared with the ground truth or manual tracing of total infarct region by two observers. The Evaluation for volumetric quantification showed good results, with high correlation between automatic and manual volume and low dispersion in Bland-Altman plot. Errors in volumetric quantification were reported in Table, with the comparison with other availablemethods. These two last methods gave almost equivalent and the lowest average error.
In addition, the evaluation for area quantification showed a tight correlation between the automatic and the manual quantification, with high agreement in Bland-Altman analysis. Average errors in infarct area quantification is compared in this Table.We can see that the proposed method gives the smallest average error amongst all methods.
Also, ourinfarct segmentation had been evaluated by Kappa coefficient.Indeed, from all the methods, ourproposed method produced the highest kappa coef., showing the most accurate infarct segmentation.
Furthermore, visual evaluation of HIA segmentation had been carried on, with the rating score from 1 (very poor) to 5 (perfect). A total of 116 images from the 20 patients were taken for visual evaluation. This table shows that the score for peri-infarct is the lowest since it is very hard to be seen precisely by human eye.
However, based on the visual evaluation… these examples show some issues in HIA segmentation.First image represented the difficulty to segment the infarct core in apical slicelikewise,, peri-infarct region was less detected here.This image shows the false positive detection for no-reflow region. The last one is an extreme case as it does not follow our assumption, where there are more than one infarct regions,appearing only in that slice, unconnected to the infarct in neighbouring slices, so that this region was failed to be detected.
Nevertheless, let’s take a look at the good examples in HIA segmentation.These three images gave excellent result with appropriate proportion of HIA. Our method also works in the no-infarct case or healthy myocardium.two regions of infarct could be detected here,, Additionaly, the advantages of applying forward and backward 3D connectivity are illustrated by these two examples, where the correct infarct region can be segmented, since other bright regions are unconnected with the detected infarct in neighboring slicesLast image show the reliability of our method in detecting the infarct when there is small difference in signal intensity.
Finally, to sum up…..in GUI software and is capable to help the clinical work of phyiscian1…to achieve more reliable results2… by implementing the forward and reverse 3D connectivity analysis. 3… indeed,Some suggestions are…1… which need to be done by the hospital2… representation of infarct segmentation3… since MATLAB have some issues with limited display resolution
Finally, to sum up…..in GUI software and is capable to help the clinical work of phyiscian1…to achieve more reliable results2… by implementing the forward and reverse 3D connectivity analysis. 3… indeed,Some suggestions are…1… which need to be done by the hospital2… representation of infarct segmentation3… since MATLAB have some issues with limited display resolution