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3D Convolutional Neural Networks for Brain Tumor Segmentation

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Authors
신동준
Advisor
유석인
Major
공과대학 컴퓨터공학부
Issue Date
2016-02
Publisher
서울대학교 대학원
Keywords
CNNMRIsegmentation
Description
학위논문 (석사)-- 서울대학교 대학원 : 컴퓨터공학부, 2016. 2. 유석인.
Abstract
Brain Tumor segmentation aims to separating the different tumor tissues (solid
or active tumor, edema, and necrosis) from normal brain tissue: gray matter
(GM), white matter (WM), and cerebrospinal fluid (CSF). In brain tumor
studies, the existence of abnormal tissues may be easily detectable most of
the time. However, accurate and reproducible segmentation and characterization
of abnormalities are not straightforward. Even for experienced doctors,
precise detection of tumor in MR image makes a long business. In recently
years, Convolutional Neural Networks (CNNs) often obtain incredible results
in problems to extract information from complex and high-dimensional inputs,
for which useful features are not obvious to be identified from the structured
design. In this thesis, we propose a new model for brain tumor segmentation
in Magnetic Resonance Imaging (MRI) by adapting multiple dimension feature
combination. First, we preprocessed original MRI meta-image in several way because MRI meta-image has much information not only brain tissue and tumor but also unnecessary noise, skull, biased intensity. Second, we built two dimensional multi-channel non-pooling convolutional neural networks. It is not
common model because almost convolutional neural networks models have focused
on natural image classification. But in medical image processing, low
level accurate segmentation is important than high level abstract. Finally, we
built a three-dimensional convolutional neural networks by combination of 2
dimensional model data and compare its result with 2 dimensional CNNs result.
The method was evaluated on BRATS 2013 (Brain Tumor Segmentation
2013) data set. This data set provided via the Virtual Skeleton Database (VSD).
Experiment results on respective datasets show the proposed algorithm works
successfully and combination of coordinate plane information improve performance
in three-dimensional segmentation problem
Language
English
URI
http://hdl.handle.net/10371/122655
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Computer Science and Engineering (컴퓨터공학부)Theses (Master's Degree_컴퓨터공학부)
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