Objective To develop and test a deep learning (DL) model for semantic segmentation of anatomical layers of the anterior chamber angle (ACA) in digital gonio-photographs. Methods and analysis We used a pilot dataset of 274 ACA sector images, annotated by expert ophthalmologists to delineate five anatomical layers: iris root, ciliary body band, scleral spur, trabecular meshwork and cornea. Narrow depth-of-field and peripheral vignetting prevented clinicians from annotating part of each image with sufficient confidence, introducing a degree of subjectivity and features correlation in the ground truth. To overcome these limitations, we present a DL model, designed and trained to perform two tasks simultaneously: (1) maximise the segmentation accuracy within the annotated region of each frame and (2) identify a region of interest (ROI) based on local image informativeness. Moreover, our calibrated model provides results interpretability returning pixel-wise classification uncertainty through Monte Carlo dropout. Results The model was trained and validated in a 5-fold cross-validation experiment on ~90% of available data, achieving ~91% average segmentation accuracy within the annotated part of each ground truth image of the hold-out test set. An appropriate ROI was successfully identified in all test frames. The uncertainty estimation module located correctly inaccuracies and errors of segmentation outputs. Conclusion The proposed model improves the only previously published work on gonio-photographs segmentation and may be a valid support for the automatic processing of these images to evaluate local tissue morphology. Uncertainty estimation is expected to facilitate acceptance of this system in clinical settings. No data are available. Not applicable.

中文翻译：

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通过自适应 ROI 定位和不确定性估计对角度照片进行语义分割

目的 开发和测试数字房角照片中前房角 (ACA) 解剖层语义分割的深度学习 (DL) 模型。方法和分析 我们使用了由 274 个 ACA 扇形图像组成的试点数据集，并由眼科专家注释来描绘五个解剖层：虹膜根、睫状体带、巩膜骨刺、小梁网和角膜。窄景深和周边渐晕使临床医生无法足够自信地注释每张图像的一部分，从而在真实情况中引入一定程度的主观性和特征相关性。为了克服这些限制，我们提出了一个 DL 模型，该模型经过设计和训练可以同时执行两项任务：(1) 最大化每帧注释区域内的分割精度；(2) 基于局部图像识别感兴趣区域 (ROI)信息性。此外，我们的校准模型提供了结果可解释性，通过蒙特卡洛丢失返回像素级分类不确定性。结果 该模型在约 90% 的可用数据上进行了 5 倍交叉验证实验的训练和验证，在保留测试集的每个地面实况图像的注释部分内实现了约 91% 的平均分割精度。在所有测试帧中成功识别了适当的 ROI。不确定性估计模块正确定位了分割输出的不准确性和错误。结论所提出的模型改进了先前发表的唯一关于角度照片分割的工作，并且可能是对这些图像的自动处理以评估局部组织形态的有效支持。不确定性估计有望促进该系统在临床环境中的接受。无可用数据。不适用。