Topological Data Analysis Guided Segment Anything Model Prompt
  Optimization for Zero-Shot Segmentation in Biological Imaging
Ruben Glatt Shusen Liu
Emerging foundation models in machine learning are models trained on vast
amounts of data that have been shown to generalize well to new tasks. Often
these models can be prompted with multi-modal inputs that range from natural
language descriptions over images to point clouds. In this paper, we propose
topological data analysis (TDA) guided prompt optimization for the Segment
Anything Model (SAM) and show preliminary results in the biological image
segmentation domain. Our approach replaces the standard grid search approach
that is used in the original implementation and finds point locations based on
their topological significance. Our results show that the TDA optimized point
cloud is much better suited for finding small objects and massively reduces
computational complexity despite the extra step in scenarios which require many
segmentations.