Self-supervised Pretraining of Cell Segmentation Models
Instance segmentation enables the analysis of spatial and temporal properties of cells in microscopy images by identifying the pixels belonging to each cell. However, progress is constrained by the scarcity of high-quality labeled microscopy datasets. Many recent approaches address this challenge by initializing models with segmentation-pretrained weights from large-scale natural-image models such as Segment Anything Model (SAM). However, representations learned from natural images often encode objectness and texture priors that are poorly aligned with microscopy data, leading to degraded performance under domain shift. We propose DINOCell, a self-supervised framework for cell instance se...
| Authors | Kaden Stillwagon, Alexandra Dunnum VandeLoo, Benjamin Magondu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 12 April 2026 |
| Last updated at source | 12 April 2026 |
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