Revisiting foundation models for cell instance segmentation

TL;DR

This paper evaluates various foundation models for cell segmentation in microscopy images, introduces an automatic prompt generation strategy to enhance model performance, and offers insights for developing more effective microscopy segmentation models.

Contribution

It provides a comprehensive evaluation of existing models and introduces APG, a novel strategy that improves segmentation results for SAM-based microscopy models.

Findings

APG improves segmentation results for μSAM

APG is competitive with CellPoseSAM

Evaluation highlights adaptation strategies for microscopy models

Abstract

Cell segmentation is a fundamental task in microscopy image analysis. Several foundation models for cell segmentation have been introduced, virtually all of them are extensions of Segment Anything Model (SAM), improving it for microscopy data. Recently, SAM2 and SAM3 have been published, further improving and extending the capabilities of general-purpose segmentation foundation models. Here, we comprehensively evaluate foundation models for cell segmentation (CellPoseSAM, CellSAM, $\mu$SAM) and for general-purpose segmentation (SAM, SAM2, SAM3) on a diverse set of (light) microscopy datasets, for tasks including cell, nucleus and organoid segmentation. Furthermore, we introduce a new instance segmentation strategy called automatic prompt generation (APG) that can be used to further improve SAM-based microscopy foundation models. APG consistently improves segmentation results for $\mu$SAM, which is used as the base model, and is competitive with the state-of-the-art model CellPoseSAM. Moreover, our work provides important lessons for adaptation strategies of SAM-style models to microscopy and provides a strategy for creating even more powerful microscopy foundation models. Our code is publicly available at https://github.com/computational-cell-analytics/micro-sam.