UNet 与 SLIC 超像素混合方法提升眼底图像脉络膜痣病灶分割

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Abstract: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.

Subjects:	Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as:	arXiv:2509.25549 [cs.CV]
	(or arXiv:2509.25549v4 [cs.CV] for this version)
	https://doi.org/10.48550/arXiv.2509.25549 arXiv-issued DOI via DataCite

Submission history

From: Mohammadmahdi Eshragh [view email]
[v1] Mon, 29 Sep 2025 22:10:56 UTC (23,620 KB)
[v2] Tue, 14 Apr 2026 18:07:05 UTC (9,916 KB)
[v3] Tue, 29 Sep 2026 17:24:34 UTC (7,100 KB)
[v4] Wed, 4 Nov 2026 21:19:58 UTC (1,107 KB)