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研究生： 賴亭諭
Lai, Ting-Yu
論文名稱： 肺結節偵測基於多重注意力機制與多尺度特徵融合之殘差架構U-Net
Lung Nodule Detection Based on The Residual U-Net with Multi-attention and Multi-scale Feature Fusion
指導教授： 徐禕佑
Hsu, Yi-Yu
學位類別： 碩士
Master
系所名稱： 敏求智慧運算學院 - 智慧科技系統碩士學位學程
MS Degree Program on Intelligent Technology Systems
論文出版年： 2023
畢業學年度： 112
語文別： 中文
論文頁數： 69
中文關鍵詞： 肺結節 、醫學影像處理 、電腦輔助診斷系統
外文關鍵詞： Lung nodules, Medical image processing, Computer-aided diagnosis system
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    • 近幾年來，電腦輔助診斷系統被應用於數種疾病，在肺癌早期階段的檢測中，我們可以藉由影像處理與深度學習技術來達成，本研究目標為偵測並標示肺部電腦斷層掃瞄影像的肺結節位置，在本研究中，將提出具備篩選以及標示肺結節位置的模型，此模型使用殘差架構U-Net，結合多尺度特徵融合（Multi-scale Feature Fusion）、空間和通道注意力機制（Spatial and Channel Attention）與Vision Transformer進行影像分割（Image segmentation）在肺影像資料庫聯盟和影像資料庫資源倡議（The lung image database consortium and image database resource initiative, LIDC-IDRI）資料集表現優良， IOU達到0.82，並且相比對照組模型，成功降低最多達39.61%的假陰性樣本，提高肺結節存在與否之判讀準確度至94.63%。

    In recent years, computer-aided diagnosis systems have been applied to various diseases. In the early detection of lung cancer, we can achieve this through image processing and deep learning techniques. The objective of this study is to detect and label the positions of lung nodules in computed tomography (CT) scan images. In this research, we propose a model with screening and marking capabilities for lung nodule positions. The model utilizes a residual architecture U-Net, incorporating multi-scale feature fusion, spatial and channel attention mechanisms, and Vision Transformer for image segmentation.

    The performance of the proposed model is evaluated on the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset, achieving excellent results. The Intersection over Union (IoU) reaches 0.82. Furthermore, compared to the control group model, our model successfully reduces up to 39.61% of false negative samples, improving the accuracy of lung nodule detection to 94.63%.

    摘要 i ABSTRACT ii 目錄 x 表目錄 xiii 圖目錄 xiv 第一章 緒論 1 1-1 研究動機與目的 1 1-2 論文架構 2 1-3 論文貢獻 3 第二章 背景介紹 4 2-1 肺結節（Pulmonary Nodule） 4 2-2 多切面電腦斷層掃描（Computerized Tomography, CT） 6 2-3 亨氏單位（Hounsfield Unit, HU） 8 第三章 相關研究 10 3-1 深度學習（Deep Learning） 10 3-1-1 人工神經網路（Artificial Neural Networks, ANN） 10 3-1-2 卷積神經網路（Convolutional Neural Networks, CNN） 11 3-1-3 循環神經網路（Recurrent Neural Networks, RNN） 13 3-2 語意分割（Semantic Segmentation） 16 3-3 U-Net 16 3-3-1 編碼器（Encoder） 17 3-3-2 解碼器（Decoder） 17 3-3-3 跳躍連接（Skip Connection） 18 3-4 Residual U-Net 18 3-5 Spatial and Channel-wise Squeeze and Excitation（scSE） 20 3-6 Vision transformer（ViT） 20 3-7 多尺度特徵融合（Multi-scale Feature Fusion） 21 第四章 研究方法 23 4-1 資料前處理 23 4-1-1 影像像素單位轉換 23 4-1-2 影像尺寸大小切割 25 4-2 肺結節分割模型之架構 26 4-3 殘差單元（Residual Unit） 27 4-4 多尺度特徵融合（Multi-scale Feature Fusion） 29 4-5 空間和通道的注意力機制（Spatial & Channel Attention） 31 4-5-1 空間擠壓與通道激發（Spatial Squeeze and Channel Excitation, cSE） 33 4-5-2 通道擠壓與空間激發（Channel Squeeze and Spatial Excitation, sSE） 34 4-6 Vision Transformer（ViT） 35 4-7 損失函數（Loss Function） 37 第五章 實驗結果 39 5-1 前言 39 5-2 資料集介紹 39 5-3 效能評估標準 40 5-3-1 Dice係數（Dice Coefficient） 40 5-3-2 IoU（Intersection over Union） 40 5-3-3 混淆矩陣（Confusion Matrix） 41 5-4 針對注意力機制之消融研究 42 5-5 針對ViT之研究 43 5-6 針對影像剪切大小之研究 44 5-7 與其他方法之比較 45 5-8 本論文二種模型之比較 47 第六章 結論 48 參考文獻 49

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