Authors
Bhat, E., Selvan, S., Okekenwa, S., Dechkounian, Z., Lin, V., Nakano, M., Saha, M., Song, Y.
Abstract
Quantitation of structures is a critical step in analyzing images. Automated segmentation of biological samples remains a central challenge in microscopy, where variations in signal/noise, intensity, texture, and edges hinder accurate delineation of cellular and tissue structures. Adaptations of foundation models such as Segment Anything Model (SAM) remain computationally intensive and require large training datasets. Here, we introduce a lightweight, open-source Google Colab pipeline that enables efficient fine-tuning of SAM2 on domain-specific datasets without additional architectural layers or specialized hardware. By coupling mask-decoder fine-tuning with biologically informed post-processing, our framework achieves robust segmentation across diverse imaging modalities. Applied to hippocampal segmentation in brain images and single-cell segmentation in cell images, fine-tuned SAM2 demonstrates substantial gains of accuracy relative to basic SAM2 and matches leading tools. This work establishes a scalable and accessible paradigm for domain-specific adaptations of SAM2 in microscopy, lowering computational and data barriers to advanced image segmentation.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 11 Nov 2025.
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