Objective-Dependent Uncertainty Driven Retinal Vessel Segmentation

TL;DR

This paper introduces a novel deep learning framework that simultaneously segments overall retinal vessels and tiny vessels by leveraging objective-dependent uncertainty, resulting in improved tiny vessel detection and overall segmentation accuracy.

Contribution

It proposes a new CNN architecture that incorporates objective-dependent uncertainty and auxiliary loss to enhance tiny vessel segmentation in retinal images.

Findings

8.3% average improvement in sensitivity for tiny vessel segmentation

Better AUC compared to state-of-the-art methods

Effective segmentation on three public datasets

Abstract

From diagnosing neovascular diseases to detecting white matter lesions, accurate tiny vessel segmentation in fundus images is critical. Promising results for accurate vessel segmentation have been known. However, their effectiveness in segmenting tiny vessels is still limited. In this paper, we study retinal vessel segmentation by incorporating tiny vessel segmentation into our framework for the overall accurate vessel segmentation. To achieve this, we propose a new deep convolutional neural network (CNN) which divides vessel segmentation into two separate objectives. Specifically, we consider the overall accurate vessel segmentation and tiny vessel segmentation as two individual objectives. Then, by exploiting the objective-dependent (homoscedastic) uncertainty, we enable the network to learn both objectives simultaneously. Further, to improve the individual objectives, we propose: (a) a vessel weight map based auxiliary loss for enhancing tiny vessel connectivity (i.e., improving tiny vessel segmentation), and (b) an enhanced encoder-decoder architecture for improved localization (i.e., for accurate vessel segmentation). Using 3 public retinal vessel segmentation datasets (CHASE_DB1, DRIVE, and STARE), we verify the superiority of our proposed framework in segmenting tiny vessels (8.3% average improvement in sensitivity) while achieving better area under the receiver operating characteristic curve (AUC) compared to state-of-the-art methods.