SAM for Robust Mitochondria Instance Segmentation in Fluorescence Microscopy
The morphological analysis of mitochondria in fluorescence microscopy (FM) is crucial for understanding cellular health, energy production, and metabolic regulation. While foundation models like the Segment Anything Model (SAM) have revolutionized natural image segmentation, their direct application to FM is hindered by a significant domain shift characterized by diffraction-limited resolution, low contrast, and complex overlapping organelle networks. Furthermore, the development of robust models is bottlenecked by a severe lack of high-quality, manually annotated instance segmentation datasets for mitochondria. In this paper, we propose a scalable solution to this data scarcity by finetu...
| Authors | Suyog Jadhav, Dilip K. Prasad, Krishna Agarwal |
|---|---|
| Primary category | cs.CV |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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