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Emerging foundation models in machine learning are models trained on vastamounts of data that have been shown to generalize well to new tasks. Oftenthese models can be prompted with multi-modal inputs that range from naturallanguage descriptions over images to point clouds. In this paper, we proposetopological data analysis (TDA) guided prompt optimization for the SegmentAnything Model (SAM) and show preliminary results in the biological imagesegmentation domain. Our approach replaces the standard grid search approachthat is used in the original implementation and finds point locations based ontheir topological significance. Our results show that the TDA optimized pointcloud is much better suited for finding small objects and massively reducescomputational complexity despite the extra step in scenarios which require manysegmentations.

Topological Data Analysis Guided Segment Anything Model Prompt Optimization for Zero-Shot Segmentation in Biological Imaging