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

Mudassir Shah1, Siyang Liu1, Lei Guo2

  • 1Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China.

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MSInet, a novel self-supervised deep learning framework, accurately segments tissues in mass spectrometry imaging (MSI) without manual labels. This method enhances spatial segmentation for better biomedical applications.

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Area of Science:

  • Biomedical Imaging
  • Computational Biology
  • Data Science

Background:

  • Mass spectrometry imaging (MSI) provides label-free molecular mapping but faces challenges in spatial segmentation due to data complexity and tissue heterogeneity.
  • Existing unsupervised clustering methods often fail to incorporate spatial information, leading to inaccurate and fragmented segmentation results.

Purpose of the Study:

  • To introduce MSInet, a self-supervised deep learning framework designed for robust and annotation-free spatial segmentation of mass spectrometry imaging data.
  • To improve the accuracy and biological relevance of MSI segmentation by integrating global and local contextual information.

Main Methods:

  • Developed MSInet, a convolutional neural network framework employing patch-wise contrastive learning for global relationships and superpixel-guided refinement for local spatial consistency.
  • Utilized a dual-consistency training strategy to enhance both global context awareness and local boundary precision.
  • Evaluated MSInet on MALDI-MSI of mouse brain, DESI-MSI of renal tumor, and synthetic datasets.

Main Results:

  • MSInet significantly outperformed state-of-the-art methods in segmentation accuracy and biological fidelity across diverse MSI datasets.
  • Achieved high performance on simulated data (Adjusted Rand Index = 0.89, Normalized Mutual Information = 0.86), showing substantial improvement over baseline methods.
  • Accurately delineated complex anatomical structures in brain tissue and distinguished critical regions in renal tumors, aligning closely with histological data.
  • Demonstrated robustness to noise inherent in MSI data.

Conclusions:

  • MSInet provides a powerful and scalable solution for accurate, biologically meaningful MSI segmentation by effectively integrating global and local contextual modeling.
  • The self-supervised, annotation-free nature of MSInet makes it broadly applicable to spatial omics and various biomedical research areas.
  • This framework represents a significant advancement in leveraging deep learning for complex biological data analysis.