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姓名	吳名頎(Ming-Chi Wu)  查詢紙本館藏	畢業系所	生物醫學工程研究所
論文名稱	肺炎診斷導向之深度學習電腦斷層掃瞄影像分割 (Pneumonia-Oriented Deep Learning Semantic Segmentation for Chest Computed Tomography)
相關論文	★ 使用滾球篩選睡眠紡錘波檢測 ★ 利用深度學習產生去骨電腦斷層掃描血管造影改進椎動脈分割 ★ 評估深度卷積神經網路用於檢測和分割Chest X-ray圖像中的鎖骨骨折 ★ 自然語言處理於病例情感分析分類器及句子相似度計算 ★ 以圓柱採樣訓練深度神經網絡改進頭頸部電腦斷層掃描的骨骼偵測和分割 ★ 使用深度學習模型自動分割黑血磁共振腦血管管壁 ★ 利用自然語言處理在胸腔X-Ray的自由文本病歷報告中標記識別心臟肥大的檔案和句子
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摘要(中)	胸部電腦斷層掃描是肺部疾病診斷的最重要的成像方式之一，肺部影像分割在電腦輔助分析和診斷過程中扮演至關重要的作用。由於深度學習模型在醫學影像解剖結構的語義分割方面的準確性已經達到了人類水平，我們提議使用經過訓練的深度學習模型來預測胸部電腦斷層切片中的肺部、肺炎感染區域和心臟位置。使用以肺炎診斷導向形式的3D影像視覺化透視人體結構繪製技術對語義分割結果進行總結和感染區域之視覺化。本次研究使用了五筆公開的胸腔電腦斷層資料集來訓練SE-ResNeXt50-FPN深度學習模型來訓練分割出胸腔電腦斷層中的肺部、感染區域和心臟位置。透過重疊率IoU與組內相關系數(ICC)的統計運算得出，模型在分割的結果上與專家所標記有很高的吻合度。我們將分割結果來生成肺炎導向的電腦輔助診斷地圖，該地圖是由3D的解剖結構的投影位置、肺總體積、和肺感染體積訊息組成，可用作為檢查胸部電腦斷層疑似肺部病變的位置指南，並可合併為診斷治療急性肺部疾病的快速分流演算法。
摘要(英)	Computer tomography (CT) scanning of the chest is one of the most important imaging modalities available for pulmonary disease diagnosis. Lung segmentation plays a crucial step in the pipeline of computer-aided analysis and diagnosis. As deep learning models have achieved human-level accuracy in semantic segmentation of anatomical structures, we propose using trained deep learning models to segment lung, heart, and the infectious areas of pneumonia in chest CT scan slices. In this study, five publicly available thoracic CT data sets were used in training and testing a deep learning semantic segmentation model, SE-ResNeXt50-FPN, for the segmentation of lungs, infected regions, and heart locations in chest CT. The statistical analyses of intersect-over-union (IoU) overlapping scores, Bland-Altman plot, and intraclass correlation coefficients (ICC) demonstrated that the segmentation results of the model were highly consistent with the annotations marked by human experts. The semantic segmentation results were summarized and visualized using volume rendering technology to generate a pneumonia-oriented interpretation map consisting of both location and volume information that can be used as location guidance for inspecting suspected pulmonary lesions of chest CT and can possibly be combined into a rapid triage algorithm for treating acute pulmonary diseases.
關鍵字(中)	★ 胸腔電腦斷層掃描 ★ 肺炎 ★ 新冠肺炎 ★ 語義分割 ★ 深度學習	關鍵字(英)	★ chest computed tomography ★ pneumonia ★ COVID-19 ★ semantic segmentation ★ deep learning
論文目次	中文摘要 i Abstract ii Table of contents iv List of Tables v List of Figures vi Chapter 1 Introduction 1 Chapter 2 Related Work 4 2.1 Semantic Segmentation 4 2.2 Computer-aided Diagnosis (CAD) 4 2.3 Application of Anatomical Structure Segmentation 5 2.3.1 Lung and Infection Lung Segmentation 5 2.3.2 Heart Segmentation 6 2.4 Maximum Intensity Projection (MIP) 7 2.5 The Evolution of Deep Learning 8 Chapter 3 Data Preprocessing and Annotation 10 3.1 Chest CT dataset 10 3.2 Lung, Infection Lung and Heart Segmentation Labeling 12 3.2.1 Lung and Infection Lung Segmentation Labeling 13 3.2.2 Heart Segmentation Labeling 14 Chapter 4 SE-ResNeXt50-FPN Architecture 15 4.1 Backbone Network 16 4.2 Semantic Segmentation head 17 Chapter 5 Software Implementation and Experiment Design 19 5.1 Software Implementation 19 5.2 Experiment Design 19 5.2.1 SE-ResNeXt50-FPN for Lung and Infection Lung Segmentation (Experiment 1) 19 5.2.2 SE-ResNeXt50-FPN for Heart Segmentation (Experiment 2) 20 5.3 Evaluation and Statistical Analysis 22 5.4 Visualization 23 Chapter 6 Results 24 Chapter 7 Discussion 37 7.1 Cases with Poor Performance in Experiments 1 and 2 37 7.2 Data Annotation Limitations 39 7.3 Future Work 40 Chapter 8 Conclusion 42 References 43
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指導教授	黃輝揚(Adam Huang)	審核日期	2023-7-14
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