标题： 学到的教训：深度网络的模块化允许跨模态重用 显示英文标题

标题： Lesson Learnt: Modularization of Deep Networks Allow Cross-Modality Reuse

摘要： 视网膜照相和光学相干断层扫描血管成像（OCT-A）是眼科影像中两种常用的模态。 随着深度学习算法的发展，视网膜图像处理，尤其是视网膜血管分割，已经得到了广泛研究。 基于已知的算子理论，构建了具有明确模块的可解释深度网络流程。 在本工作中，我们首先在视网膜数据库DRIVE上训练一个模块化的网络流程，用于视网膜血管分割任务。 然后将流程中的预处理模块直接转移到OCT-A数据上以增强图像质量，而无需进一步微调。 输出图像显示，预处理网络可以平衡对比度，抑制噪声，从而在两种图像模态中产生连接性更好的血管树。 视觉印象通过一项由五位OCT-A专家进行的观察者研究得到确认。 专家评分的统计结果显示，转移的模块提高了图像质量和诊断质量。 我们的工作提供了一个例子，说明基于已知算子理论构建的网络流程中的模块可以在不进行额外训练或迁移学习的情况下促进跨模态重用。 显示英文摘要

摘要： Fundus photography and Optical Coherence Tomography Angiography (OCT-A) are two commonly used modalities in ophthalmic imaging. With the development of deep learning algorithms, fundus image processing, especially retinal vessel segmentation, has been extensively studied. Built upon the known operator theory, interpretable deep network pipelines with well-defined modules have been constructed on fundus images. In this work, we firstly train a modularized network pipeline for the task of retinal vessel segmentation on the fundus database DRIVE. The pretrained preprocessing module from the pipeline is then directly transferred onto OCT-A data for image quality enhancement without further fine-tuning. Output images show that the preprocessing net can balance the contrast, suppress noise and thereby produce vessel trees with improved connectivity in both image modalities. The visual impression is confirmed by an observer study with five OCT-A experts. Statistics of the grades by the experts indicate that the transferred module improves both the image quality and the diagnostic quality. Our work provides an example that modules within network pipelines that are built upon the known operator theory facilitate cross-modality reuse without additional training or transfer learning.