← 研踪 PaperTrailarXiv:2509.25549 · v4
Hybrid Approach for Enhancing Lesion Segmentation in Fundus Images
首次提交 2025-09-30 06:10
版本更新 2026-11-05 05:19
雷达观测 2026-11-06 11:06
摘要
Choroidal nevi (CN) are common benign fundus lesions that require longitudinal monitoring because a small fraction transform into melanoma. Accurate delineation at native resolution is challenging: CN typically occupy <2% of the image, boundaries are indistinct, and expert annotations are limited. We propose a hybrid pipeline in which an unmodified UNet trained on 128 * 128 images provides a coarse location prior, while a training-free superpixel method (SLIC) recovers the boundary at the full input resolution. The prior further drives automatic per-image selection of the superpixel count via an image-to-lesion ratio, eliminating manual tuning. On 25 held-out 1024 * 1024 images, the proposed method achieved a mean Dice of 90.9% (IoU 87.9%), outperforming nnU-Net (83.2%), Swin UNet (73.0%), and Attention UNet (60.6%) trained at the same resolution, with all paired differences significant after Holm correction. On 90 external images from a second centre, the advantage persisted (76.8% vs. 69.3% for nnU-Net). Ablation studies identify the automated superpixel count as the key component. Inference needs no GPU, requiring 1.6 s per 1024 * 1024 image and 15 s at 3900 * 3900 on a laptop.
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v4 · 2026-11-05 05:19 · 最新观测版本
Hybrid Approach for Enhancing Lesion Segmentation in Fundus Images
Choroidal nevi (CN) are common benign fundus lesions that require longitudinal monitoring because a small fraction transform into melanoma. Accurate delineation at native resolution is challenging: CN typically occupy <2% of the image, boundaries are indistinct, and expert annotations are limited. We propose a hybrid pipeline in which an unmodified UNet trained on 128 * 128 images provides a coarse location prior, while a training-free superpixel method (SLIC) recovers the boundary at the full input resolution. The prior further drives automatic per-image selection of the superpixel count via an image-to-lesion ratio, eliminating manual tuning. On 25 held-out 1024 * 1024 images, the proposed method achieved a mean Dice of 90.9% (IoU 87.9%), outperforming nnU-Net (83.2%), Swin UNet (73.0%), and Attention UNet (60.6%) trained at the same resolution, with all paired differences significant after Holm correction. On 90 external images from a second centre, the advantage persisted (76.8% vs. 69.3% for nnU-Net). Ablation studies identify the automated superpixel count as the key component. Inference needs no GPU, requiring 1.6 s per 1024 * 1024 image and 15 s at 3900 * 3900 on a laptop.
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