Semi-Automatic Segmentation of the Right Ventricle in MR Images
(2016) In Master's Theses in Mathematical Sciences FMA820 20162Mathematics (Faculty of Engineering)
- Abstract
- Cardiac magnetic resonance is widely used to non-invasive image the heart, for both clinical and research purposes. Segmenting different parts of the heart provides important diagnostic information. Performing this task manually is time-consuming and operator dependent, hence it would be beneficial to automate this process.
The aim of this thesis is to implement an algorithm to segment the right ventricle in cardiac magnetic resonance images. The right ventricle is geometrically complex, which makes it complicated to introduce prior knowledge to aid the segmentation. Therefore, the chosen segmentation approach was the Active Shape Model (ASM). Based on training data, ASM creates a shape model that will adapt to features in an image,... (More) - Cardiac magnetic resonance is widely used to non-invasive image the heart, for both clinical and research purposes. Segmenting different parts of the heart provides important diagnostic information. Performing this task manually is time-consuming and operator dependent, hence it would be beneficial to automate this process.
The aim of this thesis is to implement an algorithm to segment the right ventricle in cardiac magnetic resonance images. The right ventricle is geometrically complex, which makes it complicated to introduce prior knowledge to aid the segmentation. Therefore, the chosen segmentation approach was the Active Shape Model (ASM). Based on training data, ASM creates a shape model that will adapt to features in an image, making it likely to segment the object of interest.
The project was conducted at the company Medviso AB, and all implementations were done in the software, Segment, a platform for cardiovascular image analysis. The data used included image sets from 115 subjects; 53 heart patients and 62 healthy volunteers. 20% of these image sets were randomly selected as test data, while the rest were used to train the shape model. As validation, segmented volumes were compared to the volumes from manual delineation, and the Dice-Sörensen coefficients were computed. The results were generally good for the mid-ventricular slices, with some errors in the most apical slices.
In conclusion, the presented approach is promising for right ventricle segmentation, but will need further improvements before final implementation
in Segment and clinical usage. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8905726
- author
- Gleisner, Gabriella LU
- supervisor
- organization
- course
- FMA820 20162
- year
- 2016
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Active Shape Model, Right Ventricle, Segmentation, Magnetic Resonance Imaging
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMA-3307-2016
- ISSN
- 1404-6342
- other publication id
- 2016:E51
- language
- English
- id
- 8905726
- date added to LUP
- 2017-05-29 16:24:32
- date last changed
- 2017-05-29 16:24:32
@misc{8905726,
abstract = {{Cardiac magnetic resonance is widely used to non-invasive image the heart, for both clinical and research purposes. Segmenting different parts of the heart provides important diagnostic information. Performing this task manually is time-consuming and operator dependent, hence it would be beneficial to automate this process.
The aim of this thesis is to implement an algorithm to segment the right ventricle in cardiac magnetic resonance images. The right ventricle is geometrically complex, which makes it complicated to introduce prior knowledge to aid the segmentation. Therefore, the chosen segmentation approach was the Active Shape Model (ASM). Based on training data, ASM creates a shape model that will adapt to features in an image, making it likely to segment the object of interest.
The project was conducted at the company Medviso AB, and all implementations were done in the software, Segment, a platform for cardiovascular image analysis. The data used included image sets from 115 subjects; 53 heart patients and 62 healthy volunteers. 20% of these image sets were randomly selected as test data, while the rest were used to train the shape model. As validation, segmented volumes were compared to the volumes from manual delineation, and the Dice-Sörensen coefficients were computed. The results were generally good for the mid-ventricular slices, with some errors in the most apical slices.
In conclusion, the presented approach is promising for right ventricle segmentation, but will need further improvements before final implementation
in Segment and clinical usage.}},
author = {{Gleisner, Gabriella}},
issn = {{1404-6342}},
language = {{eng}},
note = {{Student Paper}},
series = {{Master's Theses in Mathematical Sciences}},
title = {{Semi-Automatic Segmentation of the Right Ventricle in MR Images}},
year = {{2016}},
}