ArticleiScience2022
Scellseg: A style-aware deep learning tool for adaptive cell instance segmentation by contrastive fine-tuning.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Artificial Intelligence-Powered High-Content Analysis: Methodologies and Applications in Bioactive Compound Discovery from Natural Sources.Molecules (Basel, Switzerland) · 2026Review
- Review
- State-of-the-Art Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues.Journal of imaging · 2024Review
- Deep Learning Insights into the Dynamic Effects of Photodynamic Therapy on Cancer Cells.Pharmaceutics · 2024Article
- Microsnoop: A generalist tool for microscopy image representation.Innovation (Cambridge (Mass.)) · 2024Article
- Discovery of Natural Small Molecules Promoting Collagen Secretion by High-Throughput Screening inMolecules (Basel, Switzerland) · 2022Article
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Authors and funding
6 authors.
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Abstract
Deep learning-based cell segmentation is increasingly utilized in cell biology due to the massive accumulation of large-scale datasets and excellent progress in model architecture and instance representation. However, the development of specialist algorithms has long been hampered by a paucity of annotated training data, whereas the performance of generalist algorithms is limited without experiment-specific calibration. Here, we present Scellseg, an adaptive pipeline that utilizes a style-aware pre-trained model coupled to a contrastive fine-tuning strategy that also learns from unlabeled data. Scellseg achieves state-of-the-art transferability in average precision and Aggregated Jaccard Index on disparate datasets containing microscopy images at three biological levels, from organelle, cell to organism. Interestingly, when fine-tuning Scellseg, we show that performance plateaued after approximately eight images, implying that a specialist model can be obtained with few manual efforts. For convenient dissemination, we develop a graphical user interface that allows biologists to easily specialize their self-adaptive segmentation model.
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