Learning self-calibrated optic disc and cup segmentation from multi-rater annotations

TL;DR

This paper introduces a neural network framework that leverages multi-rater annotations for optic disc and cup segmentation, iteratively improving both the segmentation accuracy and the estimation of expert reliability.

Contribution

It proposes Diverging and Converging Models to jointly calibrate expert reliability and segmentation, outperforming existing multi-rater segmentation methods.

Findings

Self-calibrated segmentation results outperform state-of-the-art methods.

Iterative optimization improves both segmentation accuracy and expert reliability estimation.

The framework effectively handles multiple expert annotations for medical image segmentation.

Abstract

The segmentation of optic disc(OD) and optic cup(OC) from fundus images is an important fundamental task for glaucoma diagnosis. In the clinical practice, it is often necessary to collect opinions from multiple experts to obtain the final OD/OC annotation. This clinical routine helps to mitigate the individual bias. But when data is multiply annotated, standard deep learning models will be inapplicable. In this paper, we propose a novel neural network framework to learn OD/OC segmentation from multi-rater annotations. The segmentation results are self-calibrated through the iterative optimization of multi-rater expertness estimation and calibrated OD/OC segmentation. In this way, the proposed method can realize a mutual improvement of both tasks and finally obtain a refined segmentation result. Specifically, we propose Diverging Model(DivM) and Converging Model(ConM) to process the two tasks respectively. ConM segments the raw image based on the multi-rater expertness map provided by DivM. DivM generates multi-rater expertness map from the segmentation mask provided by ConM. The experiment results show that by recurrently running ConM and DivM, the results can be self-calibrated so as to outperform a range of state-of-the-art(SOTA) multi-rater segmentation methods.