Spatial self-supervised Peak Learning and correlation-based Evaluation of peak picking in Mass Spectrometry Imaging

TL;DR

This paper introduces a novel spatial self-supervised neural network for peak picking in mass spectrometry imaging, improving the selection of spatially structured peaks and providing a robust evaluation framework based on expert annotations.

Contribution

It presents a new autoencoder-based spatial self-supervised peak learning method and an evaluation procedure grounded in expert-annotated segmentation masks for MSI data.

Findings

Outperforms existing peak picking methods across diverse datasets

Selects peaks with better spatial structure preservation

Provides a robust evaluation framework for MSI peak picking methods

Abstract

Mass spectrometry imaging (MSI) enables label-free visualization of molecular distributions across tissue samples but generates large and complex datasets that require effective peak picking to reduce data size while preserving meaningful biological information. Existing peak picking approaches perform inconsistently across heterogeneous datasets, and their evaluation is often limited to synthetic data or manually selected ion images that do not fully represent real-world challenges in MSI. To address these limitations, we propose an autoencoder-based spatial self-supervised peak learning neural network that selects spatially structured peaks by learning an attention mask leveraging both spatial and spectral information. We further introduce an evaluation procedure based on expert-annotated segmentation masks, allowing a more representative and spatially grounded assessment of peak picking performance. We evaluate our approach on four diverse public MSI datasets using our proposed evaluation procedure. Our approach consistently outperforms state-of-the-art peak picking methods by selecting spatially structured peaks, thus demonstrating its efficacy. These results highlight the value of our spatial self-supervised network in comparison to contemporary state-of-the-art methods. The evaluation procedure can be readily applied to new MSI datasets, thereby providing a consistent and robust framework for the comparison of spatially structured peak picking methods across different datasets.