在青光眼检测中同时进行视杯和视盘分割的新型轻量级深度学习方法。

IF 2.6 4区 工程技术 Q1 Mathematics
Yantao Song, Wenjie Zhang, Yue Zhang
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引用次数: 0

摘要

青光眼是一种慢性神经退行性疾病,如果不在早期进行治疗,会导致不可逆的视力丧失。杯盘比是青光眼筛查和诊断的关键标准,它是通过眼底图像中视杯(OC)面积除以视盘(OD)面积确定的。因此,自动、准确地分割 OC 和 OD 是检测青光眼的关键步骤。近年来,许多方法在这项任务中取得了巨大成功。然而,大多数现有方法要么分割精度不尽人意,要么时间成本高昂。在本文中,我们提出了一种轻量级深度学习架构,用于同时分割 OC 和 OD,其中我们采用了模糊学习和多层感知器来简化学习复杂度并提高分割精度。实验结果表明,与大多数最先进的方法相比,我们提出的方法在训练时间和分割精度方面都更胜一筹。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel lightweight deep learning approach for simultaneous optic cup and optic disc segmentation in glaucoma detection.

Glaucoma is a chronic neurodegenerative disease that can result in irreversible vision loss if not treated in its early stages. The cup-to-disc ratio is a key criterion for glaucoma screening and diagnosis, and it is determined by dividing the area of the optic cup (OC) by that of the optic disc (OD) in fundus images. Consequently, the automatic and accurate segmentation of the OC and OD is a pivotal step in glaucoma detection. In recent years, numerous methods have resulted in great success on this task. However, most existing methods either have unsatisfactory segmentation accuracy or high time costs. In this paper, we propose a lightweight deep-learning architecture for the simultaneous segmentation of the OC and OD, where we have adopted fuzzy learning and a multi-layer perceptron to simplify the learning complexity and improve segmentation accuracy. Experimental results demonstrate the superiority of our proposed method as compared to most state-of-the-art approaches in terms of both training time and segmentation accuracy.

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来源期刊
Mathematical Biosciences and Engineering
Mathematical Biosciences and Engineering 工程技术-数学跨学科应用
CiteScore
3.90
自引率
7.70%
发文量
586
审稿时长
>12 weeks
期刊介绍: Mathematical Biosciences and Engineering (MBE) is an interdisciplinary Open Access journal promoting cutting-edge research, technology transfer and knowledge translation about complex data and information processing. MBE publishes Research articles (long and original research); Communications (short and novel research); Expository papers; Technology Transfer and Knowledge Translation reports (description of new technologies and products); Announcements and Industrial Progress and News (announcements and even advertisement, including major conferences).
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