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

TL;DR

This paper demonstrates that modular deep learning pipelines, built on known operator theory, can be reused across different imaging modalities like fundus and OCT-A for improved retinal image quality without retraining.

Contribution

It introduces a modular network pipeline for retinal vessel segmentation and shows its transferability across modalities, enhancing image quality without additional training.

Findings

Pretrained modules improve image contrast and noise suppression.

Transferred modules enhance vessel connectivity in OCT-A images.

Observer study confirms improved diagnostic quality.

Abstract

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.