深度學習於自動分割腦膜瘤放射手術後之水腫區域

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姓名

陳至宏(Zhi-Hong Chen) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

深度學習於自動分割腦膜瘤放射手術後之水腫區域
(Deep Learning for Automated Segmentation of Brain Edema in Meningioma after Radiosurgery)

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摘要(中)

腦膜瘤(meningioma)是最常見的良性腦部腫瘤之一，在接受加馬刀立體定位放射手術(Gamma knife radiosurgery, GKRS)治療後，可能會在病灶周邊產生一定程度的腦水腫(perifocal edema)。臨床治療上對於腦水腫體積的測定扮演著重要的角色。放射治療後病灶周邊水腫變化在腦部磁振T2權重影像(T2‐weighted)中可以清楚顯示，但是目前沒有客觀且準確的工具可以分割與量化T2權重影像中水腫的範圍與體積，所以不利於水腫消長過程、量化嚴重程度和病例差異性的研究。本研究藉由DeepMedic網路架構，和使用Mask R‐CNN模型取代手動式前處理的步驟，以遷移式學習(transfer learning)的概念訓練模型達到自動分割及量化T2權重影像中腦膜瘤GKRS後腦水腫區域。此量化工具將用以研究GKRS治療後所造成周邊組織的影響。本研究收集21位腦膜瘤患者齊接受過放射手術治療且定期追蹤的T2權重影像共154筆。經過篩選的130筆影像被隨機區分為訓練集94筆、驗證集22筆，以及測試集14筆。T2權重影像中的實際水腫範圍由臨床醫師手動標記作為監督式學習的依據。14筆測試資料經由訓練模型自動分割後，與醫師手動標記相比，達到平均相似係數(Dice similarity coefficient, DSC) 84.7%。本研究所建立的系統對於自動分割量化腦膜瘤患者接受放射手術治療後產生的腦水腫達到了相當優秀的效能，並且展露了預測模型建立的可能性。

摘要(英)

Meningioma is one of the most common benign brain tumors, the radiation to the brain tissue surround the lesions may cause different degree of perifocal edema few months after Gamma knife radiosurgery (GKRS). Volumetric assessment of perifocal edema is highly relevant for therapy planning and monitoring. The post radiosurgery brain perifocal edematous change can be clearly identified in brain magnetic resonance T2‐weighted (T2‐w) images in which it appears as more hyper‐dense area compared with normal brain tissue. However, there is a lack of an objective tool to segment and quantify the volume of these T2‐w hyper‐dense area. So, it is not conducive to the research of the process of edema growth, quantification of severity and case differences. This study trained Mask R‐CNN to replace manual pre-processing steps to generate region interest, and trained DeepMedic architecture with the concept of transfer learning to automatically segment and quantify brain edema regions in images. This quantitative result will be used to explore the research of GKRS treatment on brain edema caused by meningioma in the future. Twenty‐one patients with meningiomas who had undergone GKRS treatment, and a total of 154 regularly tracked T2‐w scans were collected. The selected 130 scans are randomly distributed to a training set of 80 scans, a validation set of 80 scans, and a test set of 20 scans. The actual range of edema in the T2‐w images is labeled manually by the clinical radiologist as a gold standard for supervised learning. The 14 test scans were automatically segmented by the trained model and compared with the manual segmentation. The average Dice similarity coefficient is 84.7%. The automatic segmentation and quantification method this research proposed for brain edema after radiosurgery demonstrates excellent results. Possibilities for building predictive models are revealed.

關鍵字(中)

★ 腦膜瘤
★ 腦水腫
★ 加馬刀放射治療
★ 深度學習
★ 腦部分割
★ 數位影像處理

關鍵字(英)

★ Meningioma
★ Brain edema
★ Gamma knife Radiosurgery
★ Deep Learning
★ Brain Segmentation
★ Digital Image Processing

論文目次

摘要 i
ABSTRACT ii
ACKNOLEDGEMENT iii
CONTENTS iv
LIST OF FIGURES vii
LIST OF TABLES x
CHAPTER 1 Introduction 1
1.1 Motivation 1
1.2 Meningiomas 2
1.3 Literature Review 3
CHAPTER 2 Materials and Methods 12
2.1 MRI Protocol 13
2.2 Dataset 15
2.3 Cross‐Validation 17
2.4 Data Pre‐Processing 20
2.4.1 Image Cropping 21
2.4.2 Voxel Resampling 21
2.4.3 Z‐Score Standardization 22
2.5 Data Augmentation 23
2.5.1 Contrast Enhancement 24
2.5.2 Elastic Transformation 25
2.6 Deep Learning 27
2.6.1 Neural Network 29
2.6.1.1 Neuron 29
2.6.1.2 Layers of Neural Network 30
2.6.2 Convolutional Neural Network 31
2.6.2.1 Convolutional Layers 32
2.6.2.2 Pooling Layers 34
2.6.2.3 Fully Connected Layers 35
2.6.3 Transfer Learning 36
2.7 Brain Extraction 37
2.7.1 Brain Masks 38
2.7.1.1 Images Reorientation 38
2.7.1.2 Split 39
2.7.1.3 Synthesize 40
2.7.1.4 Fill Manually 40
2.7.2 Mask R-CNN 41
2.7.3 Registration for GUI 43
2.8 Edema Segmentation 44
2.8.1 DeepMedic 46
2.9 Evaluation Index 48
CHAPTER 3 Experimental Results 50
3.1 Automatic Brain Extraction 50
3.1.1 Backbone 50
3.1.2 Region 51
3.1.3 Brain Extraction 53
3.1.4 Registration for GUI 57
3.2 Automatic Edema Segmentation 59
3.3 Ablation Study of Speed 64
3.4 Progression Chart 65
3.5 Graphical User Interface 67
CHAPTER 4 Discussion 68
4.1 Limitation of Dataset 68
4.2 Image Quality 69
4.3 Edema Volume 69
4.4 Registration for GUI 72
4.5 Clinical Value 73
CHAPTER 5 Conclusion and Future Work 74
5.1 Conclusion 74
5.2 Future Work 75
REFERENCES 76
APPENDIX 82
A. Environment 82
B. Main Window 83
C. Button Function 84
a. LOAD 84
b. RUN 86
c. EXPORT 89
d. SNAPSHOT 90
D. Information Bar 91
a. Patients Information 91
b. Diagnosis Information 92
c. Analysis Results Information 93
d. Other Information 94
e. Main Display Information 94
E. Operation Steps 95
a. Software start 95
b. RUN Processing 97
c. RUN Complete 97
d. Data Storage 98
F. Installation Steps 101
a. Installation 101

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指導教授

蔡章仁(Jang-Zern Tsai)

審核日期

2022-8-3

推文