Interpretable Embeddings for Segmentation-Free Single-Cell Analysis in Multiplex Imaging
This addresses the need for more robust and scalable computational pipelines in multiplex imaging for biomedical research, though it appears incremental as it builds on existing deep learning techniques.
The paper tackles the problem of cell segmentation errors in multiplex imaging by proposing a segmentation-free deep learning approach that learns interpretable embedded features, enabling accurate cell-type identification on a dataset of 1.8 million cells from neuroblastoma patients.
Multiplex Imaging (MI) enables the simultaneous visualization of multiple biological markers in separate imaging channels at subcellular resolution, providing valuable insights into cell-type heterogeneity and spatial organization. However, current computational pipelines rely on cell segmentation algorithms, which require laborious fine-tuning and can introduce downstream errors due to inaccurate single-cell representations. We propose a segmentation-free deep learning approach that leverages grouped convolutions to learn interpretable embedded features from each imaging channel, enabling robust cell-type identification without manual feature selection. Validated on an Imaging Mass Cytometry dataset of 1.8 million cells from neuroblastoma patients, our method enables the accurate identification of known cell types, showcasing its scalability and suitability for high-dimensional MI data.