Image-Level Regression for Uncertainty-Aware Retinal Image Segmentation

Trung D. Q. Dang, Huy Hoang Nguyen, Aleksei Tiulpin; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 4194-4204

Accurate retinal vessel (RV) segmentation is a crucial step in the quantitative assessment of retinal vasculature which is needed for the early detection of retinal diseases and other conditions. Numerous studies have been conducted to tackle the problem of segmenting vessels automatically using a pixel-wise classification approach. The common practice of creating ground truth labels is to categorize pixels as foreground and background. This approach is however biased and it ignores the uncertainty of a human annotator when it comes to annotating e.g. thin vessels. In this work we propose a simple and effective method that casts the RV segmentation task as an image-level regression. For this purpose we first introduce a novel Segmentation Annotation Uncertainty-Aware (SAUNA) transform which adds pixel uncertainty to the ground truth using the pixel's closeness to the annotation boundary and vessel thickness. To train our model with soft labels we generalize the earlier proposed Jaccard metric loss to arbitrary hypercubes for soft Jaccard index (Intersection-over-Union) optimization. Additionally we employ a stable version of the Focal-L1 loss for pixel-wise regression. We conduct thorough experiments and compare our method to a diverse set of baselines across 5 retinal image datasets. Our empirical results indicate that the integration of the SAUNA transform and these segmentation losses led to significant performance boosts for different segmentation models. Particularly our methodology enables UNet-like architectures to substantially outperform computational-intensive baselines. Our implementation is available at https://github.com/Oulu-IMEDS/SAUNA.