Robust Retinal Vessel Segmentation from a Data Augmentation Perspective

TL;DR

This paper introduces two novel data augmentation techniques for retinal vessel segmentation, enhancing model robustness without extra data or inference time, and demonstrates improved performance on real and synthetic datasets.

Contribution

Proposes channel-wise random Gamma correction and vessel augmentation modules to improve robustness in retinal vessel segmentation.

Findings

Enhanced segmentation accuracy on real-world datasets.

Improved robustness against image disturbances.

No additional training data or inference time required.

Abstract

Retinal vessel segmentation is a fundamental step in screening, diagnosis, and treatment of various cardiovascular and ophthalmic diseases. Robustness is one of the most critical requirements for practical utilization, since the test images may be captured using different fundus cameras, or be affected by various pathological changes. We investigate this problem from a data augmentation perspective, with the merits of no additional training data or inference time. In this paper, we propose two new data augmentation modules, namely, channel-wise random Gamma correction and channel-wise random vessel augmentation. Given a training color fundus image, the former applies random gamma correction on each color channel of the entire image, while the latter intentionally enhances or decreases only the fine-grained blood vessel regions using morphological transformations. With the additional training samples generated by applying these two modules sequentially, a model could learn more invariant and discriminating features against both global and local disturbances. Experimental results on both real-world and synthetic datasets demonstrate that our method can improve the performance and robustness of a classic convolutional neural network architecture. The source code is available at \url{https://github.com/PaddlePaddle/Research/tree/master/CV/robust_vessel_segmentation}.