系統識別號 U0026-0408202016465800
論文名稱(中文) 利用 U-Net 分割胸腔電腦斷層攝影的肺部腫瘤
論文名稱(英文) Segmentation of Lung Tumors from Chest CT Using U-Net
校院名稱 成功大學
系所名稱(中) 電腦與通信工程研究所
系所名稱(英) Institute of Computer & Communication
學年度 108
學期 2
出版年 109
研究生(中文) 謝庭瑜
研究生(英文) Ting-Yu Hsieh
學號 Q36071114
學位類別 碩士
語文別 英文
論文頁數 44頁
口試委員 指導教授-戴顯權
中文關鍵字 醫學影像  電腦斷層  肺部腫瘤分割  深度學習 
英文關鍵字 medical image  Computer Tomography  lung tumor segmentation  deep learning 
中文摘要   根據2018年死因統計報告,近年來肺癌罹患率與死亡率不斷攀升。實驗證明,利用X光影像或電腦斷層攝影,若及早發現腫瘤區域並進行追蹤檢查,可以降低肺癌高危險族群的死亡率,其中電腦斷層攝影相較於X光影像更能觀測到病變區域。
  本論文提出利用深度學習,分割出胸腔電腦斷層影像中屬於腫瘤的區域。深度學習模型主要基於U-Net而建,目標為對三維CT影像做分割,最後產生二維的結果;並同時訓練一個輔助分類器,用以濾除不包含腫瘤的影像,從而提升單一病患所有切面的分割準確率。模型採用Tversky index 作為分割結果的損失函數,可以對醫學影像這類前景和背景比例失衡的資料在訓練時做更好的優化。
英文摘要   According to the report about cause of death in 2018 in Taiwan, the morbidity and mortality rate of lung cancer are constantly increasing in recent years. A research result in America showed that diagnosis with CT in early stage relatively reduces the rate of death from lung cancer for high risk populations.
  In this Thesis, a segmentation method of lung tumors from chest CT based on U-net is proposed. The goal is to automatically segment the lung tumor region on 3D volume, then produce a mask for each slice of CT scans. The system combines the U-Net based fully convolutional network with an additional classifier block in order to eliminate the non-tumor slices as much as possible. Besides, since data imbalance is a common issue in medical image segmentation, Tversky Loss is used to optimize our model to get better performance in segmentation.
論文目次 Contents iv
List of Tables vi
List of Figures vii
Chapter 1 Introduction 1
1.1 Overview 1
Chapter 2 Background and Related Works 4
2.1 Computer Tomography (CT) 4
2.1.1 Hounsfield units (HU) 4
2.1.2 Windowing 6
2.2 U-Net 9
2.3 Attention U-Net 10
2.4 Inception-ResNet 12
2.5 Selective Kernel Network 16
2.6 Group Normalization and Batch Normalization 17
Chapter 3 The Proposed Algorithm 19
3.1 Data Preparation 21
3.1.1 Intensity Preprocessing 21
3.1.2 3D Input Volume 22
3.1.3 Image Augmentation 23
3.2 Proposed Network Architecture 24
3.2.1 Encoder Block 25
3.2.2 Reduction Block 26
3.2.3 Expansive Path 27
3.2.4 Classification Block 28
3.3 Loss Function 29
3.3.1 Tversky Loss 29
3.3.2 Binary Cross Entropy 30
3.3.3 Total Loss 31
3.4 Testing Method 31
Chapter 4 Experimental Results 32
4.1 Experimental Dataset 33
4.2 Parameter and Experimental Setting 34
4.3 Performance Evaluation 35
4.4 Experimental Results of Simulated Images 36
4.5 Failure Detection 39
Chapter 5 Conclusion and Future Work 41
5.1 Conclusion 41
5.2 Future Work 41
References 42

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