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arXiv · 2610.10306

One-Shot Adaptive Segmentation For Scientific Images

Tejaswi V. Panchagnula·Allison M. Davis·Fengqing Zhu
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Abstract

Scientific image segmentation methods rely on extensive annotation and task-specific training, limiting adaptation across imaging modalities and experimental conditions. We present a training-free, one-shot framework that specializes vision foundation models using a single annotated reference image. The framework combines DINOv3 representations with background-adaptive feature orthogonalization to suppress artifact-related feature directions, after which cosine similarity localizes candidate regions for SAM segmentation. We evaluate the framework on red-blood-cell microscopy, structured-illumination pool boiling, and chest radiography. Relative to the strongest baseline, the proposed method improves mean IoU by 5.91% and 78.62% on the microscopy and pool-boiling datasets, respectively, while achieving comparable performance on chest radiographs. These results demonstrate that one-shot reference conditioning can adapt general-purpose vision models to specialized scientific segmentation tasks.

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Tejaswi V. Panchagnula, Allison M. Davis, Fengqing Zhu. 2026-10-07. One-Shot Adaptive Segmentation For Scientific Images. https://arxiv.org/abs/2610.10306

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