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研究生: 朱楷霖
Kai-Lin Chu
論文名稱: 彈性採樣級聯深度學習應用於HER2擴增檢測和骨髓細胞分析
Soft-sampling cascade deep learning in application to the examination of HER2 amplification and bone marrow analysis
指導教授: 王靖維
Ching-Wei Wang
口試委員: 王靖維
Ching-Wei Wang
趙載光
Tai-Kuang Chao
許維君
Wei-Chun Hsu
學位類別: 碩士
Master
系所名稱: 應用科技學院 - 醫學工程研究所
Graduate Institute of Biomedical Engineering
論文出版年: 2023
畢業學年度: 111
語文別: 中文
論文頁數: 91
中文關鍵詞: 人類表皮生長因子受體第二型(HER2)擴增CEN17 和 HER2 訊號偵測轉移性乳腺癌實例分割深度學習HER2標靶治療
外文關鍵詞: HER2 amplification, CEN17 and HER2 signal detection, metastatic breast cancer, instance segmentation, deep learning, HER2 target therapy
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  • 目 錄 摘要 . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . I Abstract . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . II 致謝 . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . IV 目錄 . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . V 圖目錄 . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . VIII 表目錄 . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . X 第一章 緒論 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 研究動機 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 研究目標 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 論文貢獻 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 論文架構 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 第二章 研究背景. . . . .. . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1 樣本製備及染色方法 . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.1 螢光原位雜交法 (Fluorescence in situ hybridization, FISH) . . . 6 2.1.2 雙色原位雜交法 (Dual in situ hybridization,DISH) . . . . . . . . 7 2.1.3 骨髓之病理全景域影像 (Whole slide image, WSI) . . . . . . . . 9 2.2 研究相關方法應用之文獻 . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2.1 語義分割及物件檢測方法 . . . . . . . . . . . . . . . . . . . . 10 2.2.2 實例分割方法 . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 第三章 方法 I 實驗數據集及方法 . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.1 實驗數據集 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.2 方法 1: 擴張軟標籤 FCN2s(Dilated Soft label FCN2s) . . . . . . . . 16 3.2.1 提出的擴張軟標籤 FCN 架構 . . . . . . . . . . . . . . . . . . . 18 3.2.2 模型選擇 (Model selection) . . . . . . . . . . . . . . . . . . . . 19 3.3 其餘相關配置 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 第四章 方法實驗設計與結果 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.1 實驗資料集介紹 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.2 數據分析方法 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.2.1 FISH 螢光顯微鏡數據的數據及影像結果分析 . . . . . . . . . 25 4.2.2 DISH 光學顯微鏡數據集的結果及影像結果分析 . . . . . . . . 27 4.2.3 消融實驗 (ablation study) . . . . . . . . . . . . . . . . . . . . . 31 第五章 方法 II 實驗數據集及方法 . . . . . . . . . . . . . . . . . . . . . . . . . . 32 5.1 實驗數據集 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 5.2 方法 2: 彈性採樣級聯 (Soft-sampling cascade) 深度學習框架 . . . . . 33 5.2.1 特徵金字塔網絡(FPN)模塊 . . . . . . . . . . . . . . . . . . 35 5.2.2 檢測分支 (Detection branch) . . . . . . . . . . . . . . . . . . . 36 5.2.3 分割分支 (Segmentation branch) . . . . . . . . . . . . . . . . . 41 5.2.4 Soft-sampling cascade 深度學習方法之架構 . . . . . . . . . . . 42 5.3 偵測 CEN17 和 HER2 訊號 . . . . . . . . . . . . . . . . . . . . . . . . 44 5.4 其餘相關配置 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 第六章 方法 II 實驗設計與結果 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 6.1 實驗資料集介紹 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 6.2 數據分析方法 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 6.2.1 FISH 螢光顯微鏡數據的數據及影像結果分析 . . . . . . . . . 53 6.2.2 DISH 光學顯微鏡數據集的結果及影像結果分析 . . . . . . . . 56 6.2.3 骨髓全景域玻片數據集的結果及影像結果分析 . . . . . . . . 60 6.2.4 消融實驗 (ablation study) . . . . . . . . . . . . . . . . . . . . . 67 第七章 討論與結論及未來展望 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 7.1 討論與結論 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 7.2 未來展望 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 參考文獻 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 圖 目 錄 圖 1.1 FISH 和 DISH 的影像分析。 . . . . . . . . . . . . . . . . . . . . . 3 圖 2.1 FISH 染色處理後的玻片圖 . . . . . . . . . . . . . . . . . . . . . . . 7 圖 2.2 DISH 染色處理後的玻片圖。其中左邊為高倍率下的 DISH 影像 圖;右邊為較低倍率下的 DISH 影像圖 . . . . . . . . . . . . . . . . . 8 圖 2.3 骨髓景域影像區域示意圖 . . . . . . . . . . . . . . . . . . . . . . . 9 圖 2.4 FCN32s 架構圖 [22] . . . . . . . . . . . . . . . . . . . . . . . . . . 10 圖 2.5 Deeplabv3+ 架構圖 [29] . . . . . . . . . . . . . . . . . . . . . . . . 11 圖 2.6 彈性採樣示意圖 [30] . . . . . . . . . . . . . . . . . . . . . . . . . . 12 圖 2.7 Mask R-CNN 實例分割架構圖 [36] . . . . . . . . . . . . . . . . . . 13 圖 3.1 擴張軟標籤 FCN2s 框架架構圖 . . . . . . . . . . . . . . . . . . . . 17 圖 3.2 傳統卷積和膨脹卷積的可視化示意圖 . . . . . . . . . . . . . . . . 18 圖 4.1 混淆矩陣 (confusion matrix) . . . . . . . . . . . . . . . . . . . . . . 23 圖 4.2 DSL-FCN2s 應用在 (a)FISH 和 (b)DISH 數據集上的定量評估結果 的箱型圖 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 圖 4.3 DSL-FCN2s 應用在 (a)FISH 和 (b)DISH 數據集上的影像分割結果 30 圖 5.1 彈性採樣級聯深度學習框架的模型訓練架構圖 . . . . . . . . . . . 34 圖 5.2 特徵金字塔網絡(FPN)模塊架構圖 . . . . . . . . . . . . . . . . . 35 圖 5.3 RoI-Align 模塊解析示意圖。 . . . . . . . . . . . . . . . . . . . . . 39 圖 5.4 重疊分割示意圖。 . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 圖 5.5 在 FISH 數據集上進行 CEN17 和 HER2 信號檢測的結果。 . . . . 46 圖 5.6 在 DISH 數據集上的 CEN17 和 HER2 信號檢測結果。 . . . . . . . 47 圖 5.7 在FISH 數據集上的CEN17和HER2信號顆數示意圖ID.(HER2,CEN17)。 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 圖 5.8 在DISH 數據集上的CEN17和HER2信號顆數示意圖ID.(HER2,CEN17)。 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 圖 6.1 混淆矩陣 (confusion matrix) . . . . . . . . . . . . . . . . . . . . . . 51 圖 6.2 所提出方法應用在 FISH 數據集上的影像分割結果 . . . . . . . . . 55 圖 6.3 所提出方法應用在 DISH 數據集上的影像分割結果 . . . . . . . . . 58 圖 6.4 兩個乳癌數據集的定量評估結果的箱線圖 . . . . . . . . . . . . . . 59 圖 6.5 多類別骨髓數據集示意圖 . . . . . . . . . . . . . . . . . . . . . . . 60 圖 6.6 多類別骨髓數據集訓練時的困難點 . . . . . . . . . . . . . . . . . . 63 圖 6.7 多類別骨髓測試集 (a) 預測正確及 (b) 未預測之結果圖 (Normal Data) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 圖 6.8 多類別骨髓測試集 (a) 預測正確及 (b) 未預測之結果圖 (Disease Data) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 圖 6.9 所提出方法應用在多類別骨髓數據集的混淆矩陣 . . . . . . . . . . 66 表 目 錄 表 3-1 兩個臨床數據集的相關信息和數據分佈 . . . . . . . . . . . . . . . 15 表 3-2 所提出的 DSL-FCN2s 的綜合架構 . . . . . . . . . . . . . . . . . . 20 表 4-1 FISH 數據集之 HER2 相關細胞分割的定量評估 . . . . . . . . . . 25 表 4-2 FISH 數據集使用 LSD 檢測之統計分析表 . . . . . . . . . . . . . . 26 表 4-3 DISH 數據集之 HER2 相關細胞分割的定量評估 . . . . . . . . . . 27 表 4-4 DISH 數據集使用 LSD 檢測之統計分析表 . . . . . . . . . . . . . . 28 表 4-5 FISH 乳癌數據集使用不同網絡結構時消融研究的定量結果 . . . . 31 表 4-6 FISH 乳癌數據集使用不同網絡結構時消融研究的運行分析 . . . . 31 表 5-1 三個臨床數據集的相關信息和數據分佈 . . . . . . . . . . . . . . . 32 表 5-2 提出的 Soft-sampling cascade 深度學習之 Backbone 架構。 . . . . 42 表 5-3 提出的 Soft-sampling cascade 深度學習之分支架構 . . . . . . . . . 43 表 6-1 FISH 數據集之 HER2 相關細胞分割的定量評估 . . . . . . . . . . 53 表 6-2 在 FISH 數據集上使用 LSD 及 Tukey 檢測對分割結果進行統計分析 54 表 6-3 DISH 數據集之 HER2 相關細胞分割的定量評估 . . . . . . . . . . 56 表 6-4 在 DISH 數據集上使用 LSD 及 Tukey 檢測對分割結果進行統計 分析 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 表 6-5 進行多類別訓練之資訊及數據集數量 . . . . . . . . . . . . . . . . 60 表 6-6 多類別骨髓數據集之標註顆數 . . . . . . . . . . . . . . . . . . . . 61 表 6-7 多類別骨髓數據集之定量評估 . . . . . . . . . . . . . . . . . . . . 62 表 6-8 多類別骨髓數據集之 Normal 及 Disease data 定量分析 . . . . . . . 63 表 6-9 比較七種不同的主幹網路數據結果 . . . . . . . . . . . . . . . . . . 67 表 6-10 比較在檢測分支中使用不同的優化器和損失函數的數據結果 . . 68 表 6-11 比較使用不同的 IoU 閾值的數據結果 . . . . . . . . . . . . . . . . 68 表 6-12 比較不同的設計,將提出的 SS-SmoothL1 損失函數設計到不同 的框架上。 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

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