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

• URL: http://arxiv.org/abs/2206.05092v2
• Date: Tue, 14 Jun 2022 06:32:11 GMT
• Title: Learning self-calibrated optic disc and cup segmentation from multi-rater annotations
• Authors: Junde Wu and Huihui Fang and Fangxin Shang and Zhaowei Wang and Dalu Yang and Wenshuo Zhou and Yehui Yang and Yanwu Xu
• Abstract summary: We propose a novel neural network framework to learn OD/OC segmentation from multi-rater annotations. The proposed method can realize a mutual improvement of both tasks and finally obtain a refined segmentation result.
• Score: 7.104669952770345
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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