Training deep learning models for cell image segmentation with sparse annotations

doi:10.1101/2023.06.13.544786

Training deep learning models for cell image segmentation with sparse annotations

2023-06-13

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DOI：10.1101/2023.06.13.544786

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Deep learning is becoming more prominent in cell image analysis. However, collecting the annotated data required to train efficient deep-learning models remains a major obstacle. I demonstrate that functional performance can be achieved even with sparsely annotated data. Furthermore, I show that the selection of sparse cell annotations significantly impacts performance. I modified Cellpose and StarDist to enable training with sparsely annotated data and evaluated them in conjunction with ELE-PHANT, a cell tracking algorithm that internally uses U-Net based cell segmentation. These results illustrate that sparse annotation is a generally effective strategy in deep learning-based cell image segmentation. Finally, I demonstrate that with the help of the Segment Anything Model (SAM), it is feasible to build an effective deep learning model of cell image segmentation from scratch just in a few minutes.

Training deep learning models for cell image segmentation with sparse annotations

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