MSInet: A Self-Supervised CNN Framework Integrating Global and Local Context for Robust Mass Spectrometry Imaging Segmentation

Mass spectrometry imaging (MSI) enables label-free molecular mapping in tissues but presents challenges for spatial segmentation due to high dimensionality, nonlinear spectral variation, and tissue heterogeneity. Traditional unsupervised clustering methods often rely on predefined cluster numbers and overlook spatial information, yielding fragmented or biologically implausible results. We introduce MSInet, a self-supervised deep learning framework for robust, annotation-free MSI segmentation. MSInet combines two strategies within a convolutional neural network: patch-wise contrastive learning to capture global semantic relationships, and superpixel-guided refinement to enforce local spatial consistency. This dual-consistency design simultaneously enhances global context awareness and local boundary precision during training. MSInet was evaluated on MALDI-MSI of mouse brain, DESI-MSI of renal tumor, and a synthetic data set with ground truth. It consistently outperformed state-of-the-art methods (e.g., t-SNE + <i>k</i>-means, CNNAE + region-growing, and GCN-based models), achieving higher accuracy and biological fidelity. On simulated data, MSInet achieved an Adjusted Rand Index of 0.89 and Normalized Mutual Information of 0.86, with ∼25.8% ARI improvement over baselines. It also precisely delineated complex anatomical subregions in the brain (Silhouette Coefficient = 0.78) and distinguished tumor, necrosis, and healthy regions in renal tissues, closely aligning with histological references. MSInet further demonstrated robustness to MSI noise. By integrating global and local contextual modeling in a self-supervised architecture, MSInet offers a powerful, scalable solution for accurate and biologically meaningful MSI segmentation, with broad potential for spatial omics and biomedical applications.