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內(nèi)容簡介

　　準(zhǔn)的腦血管分割成為腦血管疾病診治的重要輔助手段，受到研究者的廣泛關(guān)注。深度學(xué)習(xí)是一種啟發(fā)式方法，它鼓勵研究人員通過驅(qū)動數(shù)據(jù)集從圖像中得出答案。隨著數(shù)據(jù)集和深度學(xué)習(xí)理論的不斷發(fā)展，在腦血管分割方面取得了重要成果。為了全面分析新的腦血管分割，本書以深度學(xué)習(xí)為核心主題，涵蓋了基于滑動窗口的模型、基于U-Net的模型、基于卷積經(jīng)網(wǎng)絡(luò)的其他模型、基于小樣本數(shù)據(jù)集的模型、基于半監(jiān)督或無監(jiān)督學(xué)習(xí)的模型、基于征融合的模型、基于Transformer的模型和基于幾何圖形學(xué)的模型。本書組織了不同模型的發(fā)展，改進(jìn)以及具體案例，探討了領(lǐng)域的發(fā)展趨勢和展望。

作者簡介

暫缺《基于語義理解和圖像分割的腦血管三維重建技術(shù)》作者簡介

圖書目錄

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