Pushing the limits of cell segmentation models for imaging mass cytometry

• URL: http://arxiv.org/abs/2402.04446v1
• Date: Tue, 6 Feb 2024 22:32:05 GMT
• Title: Pushing the limits of cell segmentation models for imaging mass cytometry
• Authors: Kimberley M. Bird, Xujiong Ye, Alan M. Race, James M. Brown
• Abstract summary: This paper explores the effects of imperfect labels on learning-based segmentation models. It evaluates the generalisability of these models to different tissue types.
• Score: 0.6003166991970345
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Imaging mass cytometry (IMC) is a relatively new technique for imaging biological tissue at subcellular resolution. In recent years, learning-based segmentation methods have enabled precise quantification of cell type and morphology, but typically rely on large datasets with fully annotated ground truth (GT) labels. This paper explores the effects of imperfect labels on learning-based segmentation models and evaluates the generalisability of these models to different tissue types. Our results show that removing 50% of cell annotations from GT masks only reduces the dice similarity coefficient (DSC) score to 0.874 (from 0.889 achieved by a model trained on fully annotated GT masks). This implies that annotation time can in fact be reduced by at least half without detrimentally affecting performance. Furthermore, training our single-tissue model on imperfect labels only decreases DSC by 0.031 on an unseen tissue type compared to its multi-tissue counterpart, with negligible qualitative differences in segmentation. Additionally, bootstrapping the worst-performing model (with 5% of cell annotations) a total of ten times improves its original DSC score of 0.720 to 0.829. These findings imply that less time and work can be put into the process of producing comparable segmentation models; this includes eliminating the need for multiple IMC tissue types during training, whilst also providing the potential for models with very few labels to improve on themselves. Source code is available on GitHub: https://github.com/kimberley/ISBI2024.

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