论文标题
与光学相干断层扫描B扫描的可区分投影无视网膜分割监督
Differentiable Projection from Optical Coherence Tomography B-Scan without Retinal Layer Segmentation Supervision
论文作者
论文摘要
来自光学相干断层扫描（OCT）B扫描的投影图（PM）是诊断视网膜疾病的重要工具，这通常需要视网膜层分割。在这项研究中，我们提出了一个新颖的端到端框架，以预测B扫描的PM。我们没有明确地将视网膜层分割，而是将它们隐式表示为预测的坐标。通过在视网膜层之间均匀采样的坐标上进行像素插值，可以通过合并轻松获得相应的PM。值得注意的是，所有操作员都是可区分的。因此，这种可区分的投影模块（DPM）可以通过PMS的基础真理而不是视网膜层进行分割实现端到端训练。我们的框架产生了高质量的PM，明显优于基线，包括没有DPM的香草CNN和基于优化的DPM而没有深层之前的优化。此外，拟议的DPM是曲线/表面之间区域/体积的新神经表示，对于几何深度学习可能具有独立的兴趣。
Projection map (PM) from optical coherence tomography (OCT) B-scan is an important tool to diagnose retinal diseases, which typically requires retinal layer segmentation. In this study, we present a novel end-to-end framework to predict PMs from B-scans. Instead of segmenting retinal layers explicitly, we represent them implicitly as predicted coordinates. By pixel interpolation on uniformly sampled coordinates between retinal layers, the corresponding PMs could be easily obtained with pooling. Notably, all the operators are differentiable; therefore, this Differentiable Projection Module (DPM) enables end-to-end training with the ground truth of PMs rather than retinal layer segmentation. Our framework produces high-quality PMs, significantly outperforming baselines, including a vanilla CNN without DPM and an optimization-based DPM without a deep prior. Furthermore, the proposed DPM, as a novel neural representation of areas/volumes between curves/surfaces, could be of independent interest for geometric deep learning.
