Retrieval-Augmented Visual Prompting: Guiding Foundation Models in Two-Photon Imaging

Two-photon calcium imaging presents a challenging setting for foundation models: image appearance varies substantially across recordings and experimental conditions, annotations are scarce, and rapid adaptation is often needed. Rather than adapting model weights through fine-tuning, we ask whether a foundation model can be guided at inference time by injecting external visual memory directly into its input. We implement this idea with SAM 3 and introduce Retrieval-Augmented Visual Prompting (RAVP), a framework in which each target tile is augmented with a retrieved annotated exemplar whose bounding box is used as a concept prompt. RAVP turns retrieval into a form of visual prompting and enables adaptation through input design alone. We study multiple exemplar selection strategies, including fluorescence-guided heuristics and a lightweight recall predictor trained to estimate which exemplar is most informative for a target tile. Experiments on the Allen Brain Observatory show that exemplar-augmented inference consistently strengthens zero-shot neuron detection and instance segmentation. Ablation studies further show that a single carefully selected exemplar is more effective than prompting with multiple retrieved examples. These results position inference-time visual memory injection as a simple and effective alternative to parameter adaptation for foundation models in specialized biomedical imaging.

ViPSAM: Visual Prompting Medical Image Segmentation Using Segment Anything Model

In proton therapy planning, respiratory-gated non-contrast CT (NCCT) is commonly used for lesion segmentation; however, accurate delineation remains challenging due to low lesion-to-background contrast. Although learning-based methods have shown strong performance, they often struggle with non-contrast image segmentation. Inspired by clinical practice, where contrast-enhanced MRI is referenced to delineate lesions on NCCT, we propose ViPSAM, a visual prompting framework that leverages complementary cross-modality information. Built upon the Segment Anything Model (SAM), ViPSAM introduces a visual prompt encoder to extract guidance features from contrast-enhanced images and a visual-guided cross-attention module to integrate non-contrast and contrast-enhanced features, thereby enhancing lesion-relevant representations in low-contrast regions. The mask decoder is further adapted in a parameter-efficient manner to utilize visual prompts effectively. We evaluate the proposed method on liver lesion segmentation using NCCT acquired for proton therapy. Experimental results demonstrate that ViPSAM outperforms representative U-Net- and SAM-based methods, indicating that cross-modality visual prompting enables more robust and accurate segmentation in non-contrast images.