Automated Virtual Pathology Panels for Mass Spectrometry Imaging

Authors

Gildenblat, J., Pahnke, J.

Abstract

Mass spectrometry imaging (MSI) records rich molecular spectra at each pixel, but pathology-oriented interpretation requires visualizations analogous to complementary histopathological stains. We present an expert-aligned framework for constructing multi-view MSI panels. Soft Landmark Contrast Edges (SoLaCE) extracts molecular boundaries directly from high-dimensional spectra. Because standard visualization metrics correlated poorly with rankings from a single expert pathologist, we combine luminance contrast and chromatic diversity with SpecEdge-Dice, a boundary-aware measure of agreement between visualization edges and SoLaCE boundaries. Parametric MiCS+LMC (pMiCS) uses a neural network trained on subsampled data to distill multiple MSI segmentations into a reusable spectral-to-RGB mapping, enabling rapid full-image inference, out-of sample projection, and more consistent color semantics across aligned images. A concept-based interpretation procedure explains pMiCS outputs through sparse mixtures of spectral concepts. In a blinded benchmark, pMiCS ranked highest among the compared methods. We integrate these components into Virtual Pathology Panels, which use hyperparameter optimization to select high-performing or spatially complementary views. This framework supports future workflows that combine morphology-oriented tissue assessment and molecular analysis within a single MSI acquisition.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 31 Jul 2026.

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