基於三維多尺度卷積神經網路自動分割與量化急性缺血性腦中風病灶

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陳孟庸(Meng-Yung Chen) 查詢紙本館藏

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論文名稱

基於三維多尺度卷積神經網路自動分割與量化急性缺血性腦中風病灶
(Automatic Acute Ischemic Lesion Segmentation and Quantification Using 3D Multi-scale Convolutional Neural Networks)

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摘要(中)

腦血管疾病，包含腦中風，為台灣十大死因的第4位。急性缺血性腦中風，又佔腦中風人數的8成以上。目前主要治療手段，是在中風後3小時內注射靜脈血栓溶解劑。若超過3小時注射，可能造成腦內出血，導致生命危險，因此治療急性缺血性腦中風是刻不容緩的。臨床醫學上，腦部磁振造影(Magnetic Resonance Imaging, MRI)是診斷急性缺血性腦中風與判定腦部梗塞區域的主要工具，其中擴散權重影像(Diffusion-Weighted Imaging, DWI)對腦梗塞區域有高敏感度。
　　若要量化腦部梗塞體積，目前仍以醫師手動標定為主，但此法既費時又繁瑣。本文提出一種在DWI上快速且自動偵測腦梗塞區域的方法。該方法分成兩個階段，第一階段使用Mask R-CNN對DWI進行腦實質提取，濾除頭骨與背景雜訊；第二階段使用3D Multi-scale CNN對DWI腦實質範圍進行腦梗塞分割，多尺度的網路架構能同時學習梗塞的大致位置和細微的特徵。在訓練兩個網路模型前，皆會進行影像標準化、影像重採樣與資料增量等影像前處理。
　　本論文使用來自台北榮民總醫院218筆DWI，其中200筆執行5-fold交叉驗證，得到平均DSC、Precision、Recall分別為74.2%、77.5%、76.2%，並且剩餘18筆作為內部測試集。最終我們將5-fold交叉驗證得到的五個模型，應用於18筆測試資料上，得到平均DSC、Precision、Recall分別為74.6%、76.7%、74.8%。同時使用來自聯新國際醫院的66筆DWI作為外部測試集，並且在五個模型上得到平均DSC、Precision、Recall分別為68.9%、65.1%、77.8%。

摘要(英)

Cerebrovascular diseases including brain strokes are the fourth leading cause of death in Taiwan. Acute ischemic strokes account for more than 80% of brain strokes. To date, the major treatment method is to inject recombinant tissue plasminogen activator (rt-PA). This treatment must be done within 3 hours after brain stroke, because injection of rt-PA later than 3 hours post-stroke may cause life-threatening cerebral hemorrhages. Hence, an accurate diagnosis of acute ischemic stroke and prompt decision for urgent treatment are very important. In clinical medicine, magnetic resonance imaging (MRI) is the most powerful tool for visualizing stroke lesions to diagnose acute ischemic stroke.
To quantify stroke infarction from MRIs, the time-consuming and cumbersome manual labeling is still the main method clinically available. This paper proposes a method for rapidly and automatically detecting infarction on DWI, which is the most sensitive to brain infarct among different MR images. This method can roughly be divided into two stages. The first stage is to use Mask R-CNN to extract brain parenchyma from DWI to eliminate the skull and extracranial noise. The second stage is to use 3D multi-scale CNN to segment brain infarction on DWI brain parenchyma. Multi-scale network architecture can learn both rough infarction positions and detail features. Before training the two neural network models, image preprocessing such as image normalization, image resampling, and data augmentation will be performed.
This study used 218 DWI scans collected from Taipei Veterans General Hospital. Among these scans, 200 were used for a 5-fold cross-validation, which resulted in a 74.2% average Dice similarity coefficient (DSC), a 77.5% average precision, and a 76.2% average recall. The remaining 18 scans were used as the internal test set to test the five generated models. The internal test resulted in a 74.6% average DSC, a 76.7% average precision, and a 76.8% average recall. Furthermore, these models were tested with 66 DWI scans from Landseed International Hospital as an external test set. The average results of DSC, precision, and recall rate were 68.9%, 65.1%, and 77.8%, respectively.

關鍵字(中)

★ 急性缺血性腦中風
★ 腦梗塞
★ 磁振造影
★ 深度學習
★ 卷積神經網路

關鍵字(英)

★ Acute Ischemic Stroke
★ Infarction
★ MRI
★ Deep Learning
★ CNN

論文目次

摘要 i
Abstract iii
誌謝 v
目錄 vii
圖目錄 xi
表目錄 xiii
一、緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究流程 4
二、文獻探討 6
2.1 急性缺血性腦中風 6
2.1.1 成因 6
2.1.2 治療方式 7
1. 靜脈血栓溶解 8
2. 動脈取栓手術 9
2.2 擴散加權成像 11
2.2.1 成像原理 11
2.2.2 技術應用 12
2.3 深度學習 13
2.3.1 Mask RCNN 14
2.3.2 三維多尺度卷積神經網路 16
2.4 相關研究 18
三、研究方法 21
3.1 受試者資料 21
3.1.1 主要研究 21
3.1.2 外部測試集 21
3.2 磁振造影拍攝參數 22
3.2.1 主要研究 22
3.2.2 外部測試集 23
3.3 腦梗塞偵測方法 24
3.3.1 Stage 1 腦實質提取 25
1. Step 1 影像格式轉換 25
2. Step 2 繪製黃金標準 26
3. Step 3 影像標準化 26
4. Step 4 Mask R-CNN模型訓練 27
3.3.2 Stage 2 腦梗塞分割與量化 28
1. Step 1 影像重採樣 28
2. Step 2 Z-score標準化 29
3. Step 3 資料增量－仿射轉換 29
4. Step 4 三維多尺度卷積神經網路模型訓練 30
3.4 資料增量 31
3.4.1 影像翻轉 32
3.4.2 灰階直方圖平移與縮放 32
3.4.3 仿射轉換 35
3.5 交叉驗證 39
3.6 評估指標 41
四、研究結果 43
4.1 腦實質提取結果 43
4.1.1 交叉驗證與測試結果 43
4.1.2 測試集詳細結果 45
4.2 腦梗塞分割結果 47
4.2.1 交叉驗證與測試結果 48
4.2.2 測試集詳細結果 49
4.2.3 外部測試集結果 52
4.2.4 與現有方法之比較 53
4.3 圖形使用者介面建置 56
4.3.1 操作流程 56
1. 讀取影像 57
2. 填寫患者資訊 58
3. 自動偵測腦梗塞 59
4. 檔案輸出 61
5. 圖形使用者介面截圖 62
五、討論 64
5.1 腦實質提取對腦梗塞分割的性能改善 64
5.2 資料增量對腦梗塞分割的性能改善 66
5.2.1 比較三種資料增量方法 66
5.2.2 仿射轉換對腦梗塞分割的性能改善 68
5.3 擴散加權影像拍攝時間對腦梗塞分割的影響 70
六、結論 73
參考文獻 75

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指導教授

蔡章仁(Jang-Zern Tsai)

審核日期

2022-8-3

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