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研究生: 王琪惠
Chi-Hui Wang
論文名稱: 基於深度學習的三維心臟磁共振影像全自動分割及應變分析
Fully automated 3D Cardiac MRI Segmentation and Strain Analysis Using Deep Learning
指導教授: 黃騰毅
Teng-Yi Huang
口試委員: 林益如
Yi-Ru Lin
蔡尚岳
Shang-Yueh Tsai
莊子肇
Tzu-Chao Chuang
學位類別: 碩士
Master
系所名稱: 電資學院 - 電機工程系
Department of Electrical Engineering
論文出版年: 2022
畢業學年度: 110
語文別: 英文
論文頁數: 47
中文關鍵詞: 深度學習心臟磁共振影像分割偽標籤跨院測試
外文關鍵詞: Deep learning, CMR segmentation, pseudo-labeling, multi-site prediction
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心臟磁共振影像的準確分割是臨床實踐中診斷和評估許多心血管疾病的重要先決條件。深度學習最近已被廣泛應用在心臟圖像分割上。本研究以基於深度學習的方法,訓練用於分割心臟左心室、心肌及右心室的模型,然而模型會受到訓練資料的內容而有所限制,因此本研究探討模型在跨院資料上的分割表現、可行性及可靠性。我們設計了幾項實驗以提升模型的分割表現,實驗方法有半監督式學習中的偽標籤方法、迭代偽標籤、邊緣標籤以及結合前面技術的最佳化模型。對於應變分析,以最佳化模型預測並產生的左心室容積的變化曲線並計算了心臟相關指標以及相對絕對體積誤差。分析結果顯示,最終提出的最佳化模型不論是在數據及影像呈現上,皆明顯優於只訓練原始資料的基本模型。


Accurate segmentation of cardiac magnetic resonance images is a prerequisite for diagnosing and assessing many major cardiovascular diseases in clinical practice. Deep learning has recently been widely used for image segmentation. This study uses a deep learning-based method to train a model for segmenting the heart's left ventricle, myocardium, and right ventricle. However, the model’s performance is limited by the content of the training data. Therefore, this study explored the model's segmentation performance, feasibility, and reliability on cross-hospital data. We designed several experiments to improve the segmentation performance of the model, including pseudo-labeling in semi-supervised learning, iterative pseudo-labeling, and edge labeling. We combined them to produce an optimized model. For strain analysis, the left ventricular volume variation curve was generated from the left ventricular volume segmented by the optimized model, and cardiac-related metrics and relative absolute volume difference were calculated. The analysis revealed that the optimized model was considerably improved from the basic model obtained from training the original data.

Abstract II 中文摘要 III List of Figures VI List of Tables VII Chapter 1. Introduction 1 1.1 Study Background 1 1.2 Deep Learning 3 1.2.1 3D U-Net 3 1.2.2 Semi-supervised learning 5 Chapter 2. Methods and Materials 7 2.1 Dataset 7 2.2 Experiments 10 2.2.1 EXP-1: RAW vs. Pseudo-labeling 10 2.2.2 EXP-2: Temporal feature 11 2.2.3 EXP-3: Iterative pseudo-labeling 12 2.2.4 EXP-4 and EXP-5: Edge labeling and COMB 14 2.2.5 EXP-6: Strain analysis 16 2.3 Evaluation 18 Chapter 3. Results 20 3.1 EXP-1 and EXP-2: RAW vs. Pseudo-labeling and Temporal feature 20 3.2 EXP-3: Iterative pseudo-labeling 22 3.3 EXP-4: Edge labeling 24 3.4 EXP-5: COMB model 25 3.5 EXP-6: Strain analysis 27 3.6 Feasibility Assessment 30 Chapter 4. Discussion & Conclusion 34 Reference 38

1. Chen, C., et al., Deep learning for cardiac image segmentation: a review. Frontiers in Cardiovascular Medicine, 2020. 7: p. 25.
2. Hammouda, K., et al., A new framework for performing cardiac strain analysis from cine MRI imaging in mice. Scientific reports, 2020. 10(1): p. 1-15.
3. Campello, V.M., et al., Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge. IEEE Transactions on Medical Imaging, 2021. 40(12): p. 3543-3554.
4. Hariharan, B., et al. Hypercolumns for object segmentation and fine-grained localization. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
5. Ronneberger, O., P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. in International Conference on Medical image computing and computer-assisted intervention. 2015. Springer.
6. Seyedhosseini, M., M. Sajjadi, and T. Tasdizen. Image segmentation with cascaded hierarchical models and logistic disjunctive normal networks. in Proceedings of the IEEE international conference on computer vision. 2013.
7. Çiçek, Ö., et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation. in International conference on medical image computing and computer-assisted intervention. 2016. Springer.
8. Sohn, K., et al., Fixmatch: Simplifying semi-supervised learning with consistency and confidence. Advances in neural information processing systems, 2020. 33: p. 596-608.
9. Lee, D.-H. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In Workshop on challenges in representation learning, ICML. 2013.
10. Rosenberg, C., M. Hebert, and H. Schneiderman, Semi-supervised self-training of object detection models. 2005.
11. Xie, Q., et al. Self-training with noisy student improves imagenet classification. in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020.
12. Ruder, S., An overview of proxy-label approaches for semi-supervised learning. 2018.
13. Bernard, O., et al., Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? IEEE Transactions on Medical Imaging, 2018. 37(11): p. 2514-2525.

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