MSCA-Net: Multi-Modal Cell Segmentation for Spatial Transcriptomics

Single-cell spatial transcriptomics has advanced spatial resolution from several cells per spot to hundreds of transcripts per cell, enabling a more comprehensive understanding of cellular interaction and local tissue organization. However, such high-resolution imaging introduces significant computational challenges, particularly in accurately segmenting cellular boundaries. Existing segmentation methods typically rely on a single modality, such as cellular imaging or transcript profiling, and thus fail to leverage the complementary information between modalities. Here we propose MSCA-Net, a Multi-Scale Convolutional Attention U-Net framework that integrates H&E staining images with selected transcriptomic features to achieve accurate cell boundary extraction. We evaluate MSCA-Net on dorsal root ganglia (DRG) neurons and demonstrate that it consistently outperforms state-of-the-art competing methods. Our study also shows that the reconstructed spatial transcriptomic slice can reproduce the downstream analysis consistent with prior knowledge, providing reliable and valuable insights for biological discovery.