提出新方法精准分割视盘与视杯，提升青光眼筛查准确性
Nested Radially Monotone Polar Occupancy Estimation: Clinically-Grounded Optic Disc and Cup Segmentation for Glaucoma Screening
- 将视盘视杯分割建模为嵌套极坐标单调占据估计
- 跨数据集零样本泛化强，在RIM-ONE上杯部Dice提升12.8%
- 保证解剖结构有效性，适合临床部署与跨域应用
从眼底照片中准确分割视盘（OD）和视杯（OC）是青光眼筛查的关键。现有深度学习方法无法保证临床解剖有效性，如星凸性与嵌套结构，尤其在跨数据集域偏移下导致诊断指标失真。本文提出NPS-Net（嵌套极形网络），首个将OD/OC分割建模为嵌套径向单调极坐标占据估计的框架。该表示形式可保障前述临床有效性并实现高精度。在七个公开数据集上评估显示，NPS-Net具备强零样本泛化能力。在RIM-ONE上保持100%解剖有效性，杯部Dice绝对提升12.8%，视杯直径比均方误差降低超56%。在PAPILA上实现视盘Dice 0.9438，视盘豪斯多夫距离95%分位数2.78像素，较最优对比方法降低83%。
原文摘要 · Abstract (English)
Valid segmentation of the optic disc (OD) and optic cup (OC) from fundus photographs is essential for glaucoma screening. Unfortunately, existing deep learning methods do not guarantee clinical validness including star-convexity and nested structure of OD and OC, resulting corruption in diagnostic metric, especially under cross-dataset domain shift. To adress this issue, this paper proposed NPS-Net (Nested Polar Shape Network), the first framework that formulates the OD/OC segmentation as nested radially monotone polar occupancy estimation.This output representation can guarantee the aforementioned clinical validness and achieve high accuracy. Evaluated across seven public datasets, NPS-Net shows strong zero-shot generalization. On RIM-ONE, it maintains 100% anatomical validity and improves Cup Dice by 12.8% absolute over the best baseline, reducing vCDR MAE by over 56%. On PAPILA, it achieves Disc Dice of 0.9438 and Disc HD95 of 2.78 px, an 83% reduction over the best competing method.
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