通过整合杯盘比和神经视网膜边缘特征的高级分割增强青光眼分类

IF 5.4 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Rabia Pannu , Muhammad Zubair , Muhammad Owais , Shoaib Hassan , Muhammad Umair , Syed Muhammad Usman , Mousa Ahmed Albashrawi , Irfan Hussain
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引用次数: 0

摘要

青光眼是一种由高眼压引起的进行性眼病,损害视神经,导致逐渐的、不可逆的视力丧失,通常没有明显的症状。轻微的眼睛发红、轻微的视力模糊和眼睛疼痛等细微的迹象可能会被忽视,因此它被称为“无声的视觉窃贼”。受预期寿命延长的推动,随着人口老龄化,其患病率正在上升。大多数计算机辅助诊断(CAD)系统依靠杯盘比(CDR)进行青光眼诊断。本研究引入了一种将CDR与神经视网膜边缘比(NRR)相结合的新方法,该方法量化了视盘(OD)内边缘的厚度。NRR通过捕捉额外的视神经头部变化(如边缘变薄和组织丢失)提高了诊断的准确性,这些变化在单独使用CDR时被忽视。一种改进的用于OD和光杯(OC)分割的ResUNet架构,结合残差学习和U-Net捕获空间上下文进行语义分割。对于OC分割,模型对DRISHTI-GS和RIM-ONE的Dice Coefficient (DC)得分分别为0.942和0.872,Intersection over Union (IoU)值分别为0.891和0.773。对于OD分割,DRISHTI-GS和RIM-ONE的DC分别为0.972和0.950,IoU分别为0.945和0.940。对ORIGA和REFUGE的外部评价证实了模型的鲁棒性和泛化性。从分割掩模中计算CDR和NRR,并使用径向基函数训练SVM,对健康眼和青光眼进行分类。模型在DRISHTI-GS和RIM-ONE上的精度分别为0.969和0.977。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhanced glaucoma classification through advanced segmentation by integrating cup-to-disc ratio and neuro-retinal rim features
Glaucoma is a progressive eye condition caused by high intraocular fluid pressure, damaging the optic nerve, leading to gradual, irreversible vision loss, often without noticeable symptoms. Subtle signs like mild eye redness, slightly blurred vision, and eye pain may go unnoticed, earning it the nickname “silent thief of sight.” Its prevalence is rising with an aging population, driven by increased life expectancy. Most computer-aided diagnosis (CAD) systems rely on the cup-to-disc ratio (CDR) for glaucoma diagnosis. This study introduces a novel approach by integrating CDR with the neuro-retinal rim ratio (NRR), which quantifies rim thickness within the optic disc (OD). NRR enhances diagnostic accuracy by capturing additional optic nerve head changes, such as rim thinning and tissue loss, which were overlooked using CDR alone. A modified ResUNet architecture for OD and optic cup (OC) segmentation, combining residual learning and U-Net to capture spatial context for semantic segmentation. For OC segmentation, the model achieved Dice Coefficient (DC) scores of 0.942 and 0.872 and Intersection over Union (IoU) values of 0.891 and 0.773 for DRISHTI-GS and RIM-ONE, respectively. For OD segmentation, the model achieved DC of 0.972 and 0.950 and IoU values of 0.945 and 0.940 for DRISHTI-GS and RIM-ONE, respectively. External evaluation on ORIGA and REFUGE confirmed the model’s robustness and generalizability. CDR and NRR were calculated from segmentation masks and used to train an SVM with a radial basis function, classifying the eyes as healthy or glaucomatous. The model achieved accuracies of 0.969 on DRISHTI-GS and 0.977 on RIM-ONE.
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来源期刊
CiteScore
10.70
自引率
3.50%
发文量
71
审稿时长
26 days
期刊介绍: The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.
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