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Multi Dimensional LSTM for 3D patch based pancreas segmentation

Title
Multi Dimensional LSTM for 3D patch based pancreas segmentation
Translated Title
다차원 장기-단기 기억장치를 사용한 3차원 패치기반 췌장 영역화
Authors
Kim, Jeong Hwan
DGIST Authors
Kim, Jeong Hwan; Park, Sang Hyun; Ye, Dong Hye
Advisor(s)
박상현
Co-Advisor(s)
Ye, Dong Hye
Issue Date
2020
Available Date
2020-08-06
Degree Date
2020/08
Type
Thesis
Description
Computed Tomography, Pancreas segmentation, Convolutional Neural Network, Long Short-Term Memory
Abstract
Compute Tomography (CT) imaging is mostly used to diagnose abdominal disease. Among many organs in abdominal CT, the pancreas is one of important regions to diagnose diabetes, pancreatic cancer, and pancreatitis. Thus, it is important to fin the pancreas area and quantitatively analyze the present of disease. However, it is difficult to segment the pancreas area due to the size and shape variation depending on patients and ambiguous boundaries with surrounding organs. A lot of methods have been proposed for the automatic pancreas segmentation, but the accurate segmentation is still challenging. Recently, deep learning methods have been achieved better performance than conventional machine learning based segmentation methods for many applications. Thus, in this paper, we propose a new pancreas segmentation method using deep learning. Applying a deep learning algorithm to the 3D CT image segmentation is non-trivial due to a memory limitation. Several deep learning methods address this issue by dividing a 3D medical image into small 3D patches and then applying 3D Convolutional Neural Networks (CNN) on the patches. However, anatomical information cannot be considered in this way. To address these issues, in this paper, we propose a Multi-Dimensional Long Short-Term Memory (MDLSTM) method which can consider the anatomical information using a sequential learning scheme. MDLSTM is compose of a 3D CNN that performs the patch-wise segmentation on local regions and a Long Short-Term Memory (LSTM) that propagates information of adjacent patches. For evaluation, the NIH CT dataset was used to train the MDLSTM model, and Dice-Sorensen Coefficient (DSC), Precision, and Recall were used to compare performance with other 2D and 3D CNN methods. When using the proposed method, we can see the DSC and precision were increased by 2%, 16%, respectively compared to the conventional CNN method.
Table Of Contents
List of Contents Abstract ·································································································· i List of contents ························································································· ii List of tables ··························································································· iii List of figures ·························································································· iv Ⅰ. Introduction 1.1 Introduction ·················································································· 1 1.2 Related works ················································································ 2 Ⅱ. Method 2.1 U-net ·························································································· 4 2.2 Convolutional Long Short-Term Memory ··············································· 5 2.3 Multi-Dimensional Convolutional Long Short-Term Memory ························ 6 2.4 Forward and Backward propagation for the MDCLSTM network ···················· 7 Ⅲ. Experimental results 3.1 Dataset ························································································ 8 3.2 Implementation details ······································································ 8 3.3 Evaluation metrics ··········································································· 8 3.3 Qualitative and Quantitative Analysis ···················································· 9 Ⅵ. Conclusion ······················································································· 12 Reference ······························································································ 13
URI
http://dgist.dcollection.net/common/orgView/200000334023
http://hdl.handle.net/20.500.11750/12166
DOI
https://doi.org/10.22677/thesis.200000334023
Degree
Master
Department
Department of Robotics Engineering
University
DGIST
Related Researcher
  • Author Park, Sang Hyun Medical Image & Signal Processing Lab
  • Research Interests 컴퓨터비전, 인공지능, 의료영상처리
Files:
Collection:
Department of Robotics EngineeringThesesMaster


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