MSSOrg: a multi-scale SSM-based model for organoid location and classification

Xun Deng, Zhibin Li, Mei Gao, Shaolin Liang, Tianyu Sun, Zhipeng Shang, Yuhang Yang, Yafang Wei, Lun Hu, Pengwei Hu

Abstract: Organoids, 3D in vitro cultures replicating their tissue or organ of origin, hold great potential for disease research, drug testing, and regenerative medicine. However, current analysis methods primarily rely on staining techniques, which limit high-throughput analysis and require manual adjustments. Automated and non-invasive methods remain underexplored, and most rely on small-scale datasets that fail to capture the complexity of organoids, which vary significantly in shape and size and have more intricate backgrounds than cells. To address these limitations, we developed MSSOrg, a multiscale framework for organoid localization and classification, integrating Local and Global State Space Models (SSMs) to balance pixel dependencies and texture features. MSSOrg mitigates background interference and image noise in bright-field images, achieving superior performance in both organoid detection and morphological classification. This innovation enables real-time, large-scale organoid analysis, providing a powerful tool for drug screening and disease research, ultimately accelerating progress in these fields.