Training deep learning models for cell image segmentation with sparse annotations

Sugawara, K.

doi:10.1101/2023.06.13.544786

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bioRxiv · 10.1101/2023.06.13.544786

Training deep learning models for cell image segmentation with sparse annotations

Sugawara, K.

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Abstract

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.

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Sugawara, K.. 2023-06-13. Training deep learning models for cell image segmentation with sparse annotations. https://doi.org/10.1101/2023.06.13.544786

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