Chapter 1 Introduction for Cerebrovascular Segmentation 1
1.1 Overview 1
1.2 Background 2
1.3 Cerebrovascular Imaging Modalities 6
1.4 Open Source for Medical Images Segmentation 9
1.5 Discussion of Development Trend 12
1.6 Discussion of Quantitative Assessment 13
1.7 Challenges and Opportunities 16
1.8 Conclusions 17
Chapter 2 DL-based Cerebrovascular Segmentation Model 19
2.1 Sliding Window Based Models 19
2.2 U-Net Based Models 20
2.3 Other CNNs Based Models 24
2.4 Small-Sample Based Models 26
2.5 Semi-Supervised / Unsupervised Learning Models 28
2.6 Fusion Based Models 30
2.7 Transformer Based Models 31
2.8 Graphics Based Models 32
Chapter 3 Generative Consistency for Semi-Supervised Learning
Cerebrovascular Segmentation from TOF-MRA 35
3.1 Overview 35
3.2 Introduction 36
3.3 Methods 39
3.4 Experiments 47
3.5 Discussion 51
3.6 Conclusion 55
Chapter 4 A Learnable Gabor Convolution Kernel for Vessel
Segmentation 57
4.1 Overview 57
4.2 Introduction 58
4.3 Methods 60
4.4 Experiments and Discussion 69
4.5 Conclusion 79
Chapter 5 Integration-and Separation-Aware Adversarial Model
for Cerebrovascular Segmentation from TOF-MRA 80
5.1 Overview 80
5.2 Introduction 81
5.3 Methods 84
5.4 Datasets 90
5.5 Experiments and Results 90
5.6 Discussion 96
5.7 Conclusion 100
Chapter 6 Cerebrovascular Segmentation in Phase-Contrast
Magnetic Resonance Angiography by Multi-Feature
Fusion and Vessel Completion 101
6.1 Overview 101
6.2 Introduction 102
6.3 Methods 105
6.4 Results 113
6.5 Discussion 116
6.6 Conclusion 126
References 128
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