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Patch wise 3D CNN with transition layer and normalized loss function for MRI brain segmentation

Title
Patch wise 3D CNN with transition layer and normalized loss function for MRI brain segmentation
Authors
MIGUEL ANDRES LUNA ACEVEDO
DGIST Authors
ACEVEDO, MIGUEL ANDRES LUNA; Kim, Jaeil; Park, Sanghyun
Advisor(s)
박상현
Co-Advisor(s)
Jaeil Kim
Issue Date
2019
Available Date
2019-10-03
Degree Date
2019-02
Type
Thesis
Abstract
In recent years, Convolutional Neural Networks (CNN) have been applied to a wide range of computer vision tasks thanks to their ability to learn informative features from data without human supervision. CNN models have been successfully utilized to segment and classify natural images where there are large datasets, nonetheless, many structures have been proposed to improve their performances. By contrast, in the medical field even though datasets are smaller than their natural images counterpart, CNN models have also over-perform previous methods and are becoming the first choice to tackle a variety of probleMaster. However, it is unclear how CNN structures are able to make a better use of the limited training data to generalize and produce reliable predictions. Therefore, in this study a set of CNN structures was analyzed on healthy brain tissue segmentation as well as brain abnormalities segmentation to understand the effect of the architecture design and the loss function used to train the model. The main contributions of the present thesis are the proposed CNN architecture that is capable of segmenting the brain across different tasks, the proposed loss function that increases the accuracy and reduces the number of false negatives and the segmentation quality analysis across different metrics that allow us to understand the advantages and disadvantages of a CNN model.
Table Of Contents
I. INTRODUCTION - 1 II. BASIC CONCEPTS AND BACKGROUND - 2 1 MRI IMAGES - 2 1.1 T1 WEIGHTED SEQUENCES - 3 1.2 T1 POST CONTRAST - 4 1.3 T1 WEIGHTED INVERSION RECOVERY - 4 1.4 T2 WEIGHTED SEQUENCES - 4 1.5 FLAIR - 5 2 BRAIN MRI SEGMENTATION - 5 2.1 COMPUTER ASSISTED SEGMENTATION - 5 2.2 BRAIN MRI SEGMENTATION PROCESS - 6 2.3 TRADITIONAL SEGMENTATION METHODS - 8 2.4 DEEP LEARNING SEGMENTATION METHODS - 9 2.5 EVALUATION - 11 2.6 LIMITATIONS - 12 III. METHOD - 15 1 PROPOSED MODEL - 15 2 VARIATIONS OF PROPOSED MODEL - 17 2.1 Model A - 17 2.2 Model B - 17 2.3 Model C - 17 2.4 Model D - 17 2.5 Model E - 18 2.6 Model F - 18 IV. EXPERIMENTAL RESULTS - 20 1 BRAIN TUMOR SEGMENTATION - 20 1.1 BraTS 2018 challenge description - 20 1.2 BraTS experiments - 21 2 WHOLE BRAIN SEGMENTATION - 24 2.1 MR brain segmentation challenge 2018 description - 25 2.2 Brain segmentation experiment - 26 3 WMH SEGMENTATION - 27 3.1 WMH segmentation challenge 2017 description - 28 3.2 WMH segmentation experiments - 29 V. DISCUSSION AND CONCLUSION - 32 References - 33
URI
http://dgist.dcollection.net/common/orgView/200000171495
http://hdl.handle.net/20.500.11750/10752
DOI
10.22677/thesis.200000171495
Degree
MASTER
Department
Robotics Engineering
University
DGIST
Related Researcher
  • Author Park, Sang Hyun Medical Image & Signal Processing Lab
  • Research Interests 컴퓨터비전, 인공지능, 의료영상처리
Files:
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Collection:
Department of Robotics EngineeringThesesMaster


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