508 - SSL-CST: Cell Segmentation for Single-Cell Spatial Transcriptome Based on Self-Supervised Learning

The continuous advancements in life science technology have enabled spatial transcriptome technology to achieve an impressive level of resolution at the single-cell level. This technology has emerged as a crucial method for studying the cellular composition and differentiation states of tissues, investigating cell-cell interactions, and unraveling the molecular mechanisms underlying diseases and developmental processes. A key component in this analysis is the accurate segmentation of cells. However, existing segmentation methods often fail to fully leverage the valuable information provided by spatial transcriptomics, leading to inaccurate cell segmentation. In this study, we introduce SSL-CST, a cell segmentation for single-cell spatial transcriptome method based on self-supervised learning. SSL-CST employs a pre-trained model for foundational contour segmentation. Following the denoising process, it utilizes a self-supervised neural network to correct the cell boundaries to obtain accurate cell boundaries. Through this approach, SSL-CST outperforms other state-of-the-art methods in various tests conducted on multiple datasets. The improved segmentation provided by SSL-CST further enhances the analysis of single-cell spatial expression, providing effective tools for biological discovery.