深度卷积神经网络定量评价角膜后弹力层内皮移植术后植片脱离
Quantifying Graft Detachment after Descemet's Membrane Endothelial Keratoplasty with Deep Convolutional Neural Networks
摘要：
目的：我们开发了一种方法来自动定位和量化后弹力层内皮角膜移植术（DMEK）后的前节光学相干断层扫描（AS-OCT）扫描移植物脱离。方法：由DMEK专家对1280例AS-OCT B扫描进行注释。使用注释，开发了深度学习管道来定位巩膜突，使AS-OCT B扫描居中并分割分离的移植物部分。通过比较（1）分离长度和（2）分离切片的水平投影与专家注释，根据B扫描评价分离分割模型性能。水平投影用于构建移植物脱离图。所有最终评估都是在模型训练期间分开设置的测试集上进行的。第二位DMEK专家对测试集进行了注释，以确定评分员之间的表现。结果如下：平均巩膜突定位误差为0.155 mm，而评价者间差异为0.090 mm。69%的病例中，估计的移植物脱离长度与地面真实值的差异在10像素（~150μm）范围内（第二位DMEK专家为77%）。对于我们的模型和第二位DMEK专家，所有B扫描的水平投影的Dice评分分别为0.896和0.880。结论：我们的深度学习模型可用于自动和即时定位AS-OCT B扫描中的移植物脱离。水平脱离投影可以与人类DMEK专家相同的精度确定，从而允许构建准确的移植物脱离图。翻译相关性：移植物脱离的自动定位和量化可以支持DMEK研究并标准化临床决策。
英文摘要：
Purpose: We developed a method to automatically locate and quantify graft detachment after Descemet's Membrane Endothelial Keratoplasty (DMEK) in Anterior Segment Optical Coherence Tomography (AS-OCT) scans. Methods: 1280 AS-OCT B-scans were annotated by a DMEK expert. Using the annotations, a deep learning pipeline was developed to localize scleral spur, center the AS-OCT B-scans and segment the detached graft sections. Detachment segmentation model performance was evaluated per B-scan by comparing (1) length of detachment and (2) horizontal projection of the detached sections with the expert annotations. Horizontal projections were used to construct graft detachment maps. All final evaluations were done on a test set that was set apart during training of the models. A second DMEK expert annotated the test set to determine inter-rater performance. Results: Mean scleral spur localization error was 0.155 mm, whereas the inter-rater difference was 0.090 mm. The estimated graft detachment lengths were in 69% of the cases within a 10-pixel (~150μm) difference from the ground truth (77% for the second DMEK expert). Dice scores for the horizontal projections of all B-scans with detachments were 0.896 and 0.880 for our model and the second DMEK expert respectively. Conclusion: Our deep learning model can be used to automatically and instantly localize graft detachment in AS-OCT B-scans. Horizontal detachment projections can be determined with the same accuracy as a human DMEK expert, allowing for the construction of accurate graft detachment maps. Translational Relevance: Automated localization and quantification of graft detachment can support DMEK research and standardize clinical decision making.
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