基於三維多尺度卷積神經網路自動分割與量化腦海綿狀血管瘤

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姓名

姜智桓(Zhi-Huan Jiang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

基於三維多尺度卷積神經網路自動分割與量化腦海綿狀血管瘤
(Automated Cerebral Cavernous Malformation Segmentation and Quantification Using 3D Multi-scale Convolutional Neural Networks)

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至系統瀏覽論文 (2025-8-1以後開放)

摘要(中)

腦海綿狀血管瘤（cerebral cavernous malformations, CCM），是腦部中的一種血管病變，由良性不正常的血管組成，在腦部某一位置的血管膨脹成團。在T2權重影像上會有低信號hypointensity（黑色）的邊緣，本體可能因為反覆出血而呈現多囊狀有如爆米花(popcorn)般的型態。目前腦海綿狀血管瘤的診斷，主要依賴醫師的目視判讀及手動標記，但人眼判讀的方法容易受外界環境、視覺疲勞影響，手動標記耗時費力，因此擁有一客觀工具來改善診斷的準確性和效率為當前需求。本文提出一種深度學習方式，在T2權重影像上自動分割及量化腦海綿狀血管瘤。首先，使用Mask Region based Convolution Neural Networks (Mask RCNN)對T2權重影像進行實質腦提取，去除頭骨、頭皮以及背景雜訊，目的為提高分割效率於實質腦範圍內的腦海綿狀血管瘤，接著對影像進行強度標準化、體素尺寸重採樣以及資料增量等影像前處理，最後使用Deepmedic多尺度3D卷積神經網路在實質腦範圍進行腦海綿狀血管瘤的分割與量化。本研究使用的資料來源為臺北榮民總醫院192筆T2權重影像，資料被隨機劃分五分之三為訓練集、五分之一為驗證集及五分之一為測試集。目前訓練模型用於腦海綿狀血管瘤自動分割在測試集上取得的模型評估指標，平均Dice、精確率(Precision)、召回率(Recall)分別為0.736、0.807和0.729。此結果顯示了所提出的深度學習方法在自動腦海綿狀血管瘤分割方面的有效性。此系統的開發提供了一種客觀工具，以提高腦海綿狀血管瘤診斷的準確性和效率。

摘要(英)

Cerebral cavernous malformations (CCM) are vascular abnormalities in the brain characterized by benign clusters of abnormal blood vessels. Magnetic resonance imaging (MRI) is a diagnostic tool used by physicians to detect and assess the size of CCM. In T2-Weighted (T2W) images, there may be hypointensity (dark) edges, and the lesion itself may appear as a multicystic structure resembling popcorn-like morphology, possibly due to recurrent seizures. Currently, the diagnosis of CCM heavily relies on visual interpretation and manual delineation by physicians. However, these methods are subjective, prone to environmental factors and visual fatigue, and time-consuming. Therefore, there is a current need for an objective tool to improve the accuracy and efficiency of diagnosis. To address these challenges, we proposed a deep learning-based approach for automated segmentation and quantification of CCM on T2W. Firstly, a Mask Region based Convolution. Neural Networks (Mask RCNN) model is employed to extract the brain region from the T2W, removing skull, scalp, and background noise to improve segmentation efficiency within the brain region. The images are then subjected to preprocessing steps including intensity normalization, voxel size resampling, and data augmentation. Finally, a Deepmedic multi-scale 3D convolutional neural network (CNN) is used to perform CCM segmentation and quantification within the extracted brain region. The dataset used in this study consists of 192 T2W from Taipei Veterans General Hospital, which are randomly divided into training (3/5), validation (1/5), and testing (1/5) sets. The trained model for CCM segmentation achieved the following evaluation metrics on the testing set: average Dice coefficient of 0.736, precision of 0.807, and recall of 0.729. The results demonstrate the effectiveness of the proposed deep learning approach in automated CCM segmentation. The developed system provides an objective tool to improve the accuracy and efficiency of CCM diagnosis.

關鍵字(中)

★ 腦海綿狀血管瘤
★ 磁振造影
★ 深度學習
★ 自動分割
★ 3D卷積神經網路

關鍵字(英)

★ Cerebral cavernous malformation
★ Magnetic resonance imaging
★ Deep Learning
★ Segmentation
★ 3D convolutional neural network

論文目次

摘要 i
ABSTRACT iii
CONTENTS v
LIST OF FIGURES viii
LIST OF TABLES x
CHAPTER 1 Introduction 1
1.1 Cerebral Cavernous Malformation 1
1.2 Clinical Needs 1
1.3 Automatic Segmentation of CCM 2
CHAPTER 2 Literature review 3
2.1 Related Literature on CCM Segmentation 3
2.2 Literature on Brain Tumor Segmentation 3
2.3 Literature on Brain Extraction 7
CHAPTER 3 Materials and Methods 9
3.1 T2 Weighted Image 10
3.2 Dataset 10
3.3 MRI Protocol 12
3.4 K-fold Cross-Validation 12
3.5 Brain Extraction 13
3.5.1 Gold Standard 14
3.5.2 Mask Region based Convolution Neural Networks (Mask RCNN) 15
3.6 Pre-processing 16
3.6.1 Voxel size Resampling 17
3.6.2 Intensity Normalization 17
3.6.3 Data Augmentation 18
3.7 CCM Segmentation 18
3.7.1 Deepmedic 18
3.7.2 Hyperparameters 19
3.8 Performance Evaluation 20
CHAPTER 4 Experimental Results 23
4.1 Brain Extraction 23
4.2 CCM Segmentation 25
4.2.1 Voxel-level 26
4.2.2 Number-level 29
4.3 Graphical User Interface 31
CHAPTER 5 Discussion 32
5.1 CCM Segmentation and Quantification 32
5.2 Special Cases in Data 32
5.2.1 Conservative Treatment of CCM 33
5.2.2 Selected Treatment of CCM 34
5.2.3 CCM with Hemorrhage 35
5.2.4 Expanded Treatment of CCM 36
5.2.5 Revised Ground Truth with T1-Weighted Contrast-Enhanced (T1C) 38
5.3 T2W vs. T2W + T1C 39
5.3.1 Preparation of T1C 39
5.3.2 CCM Segmentation Comparison 41
5.3.3 Model Selection for CCM Segmentation 44
5.4 Result Comparison with Literature 46
5.5 Clinical Application for Seizure Incidence Prediction 48
CHAPTER 6 Conclusion and Future Work 50
6.1 Conclusion 50
6.2 Future Work 50
REFERENCES 51
APPENDIX 55
A. Environment 55
B. Interface & Button Introduction 56
a. Main Window 56
b. Load 56
c. RUN 57
d. EXPORT 57
e. SNAPSHOT 58
C. Information Bar 59
a. Patients Information 59
1. ID 59
2. Name 59
3. Gender 60
4. DOB 60
5. GK Date 60
b. Diagnosis Information 60
c. Results Information 61
D. Main Display Information 62
a. T2W Display 62
b. CCM Segmentation Display 63
E. Mouse Function Introduction 63
a. Slider 63
F. Operation Steps 64
a. LOAD Step 64
b. LOAD Step done 65
c. RUN Step 65
d. RUN Step done 66
e. EXPORT Step 67
f. EXPORT Step done 68
g. SNAPSHOT Step 69
h. SNAPSHOT Step done 70

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指導教授

蔡章仁(Jang-Zern Tsai)

審核日期

2023-7-28

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