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

Philipp Weigand; Nikolas Ebert; Shad A. Mohammed; Denis Abu Sammour; Carsten Hopf; Oliver Wasenmüller

cs.CVMar 11, 2026

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

Authors: Philipp Weigand, Nikolas Ebert, Shad A. Mohammed, Denis Abu Sammour, Carsten Hopf, Oliver Wasenmüller

Organizations: Research and Transfer Center CeMOS, Technical University of Applied Sciences Mannheim, Paul-Wittsack-Straße 10, Mannheim, 68163, Germany · Faculty of Biosciences Heidelberg University, Im Neuenheimer Feld 234, Heidelberg, 69120, Germany · Mannheim Center for Translational Neurosciences (MCTN), Med. Faculty Mannheim, Heidelberg University, Theodor Kutzer-Ufer 1-3, 68167, Mannheim, Germany

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.

Figures & tables

Method name	Input	Functionality	Quantitative evaluation
MALDIquant [ 16 ] / Cardinal [ 11 ]	Single spectra	Savitzky-Golay-Filter, S/N-Thresholds	✗
msiPL [ 1 ]	Single spectra	Variational Autoencoder	✗
Lieb et al. [ 24 ]	Spatio-spectral	Sparse frame multipliers	✓
SPUTNIK [ 19 ]	Ion images	Image filters, PCA-image correlation	✗

Table 1: Overview of existing state-of-the-art peak picking methods [ 16 , 24 , 19 , 1 ] .

Figure 1: Our proposed spatial self-supervised peak learning pipeline, S 3 PL, for spatially structured peak picking. The network receives an input comprising a spectral patch

x

of a complete spectrum and its neighboring spectra. During training, the network learns to identify the most informative peaks by learning an attention mask. In order to identify the peaks, the frozen 3D convolution is applied to the entire dataset, resulting in the filtering of

z

peaks per spectral patch

x

. In the final selection, the

n

most frequent peaks of the peak selection yield the final peak list.

Figure 2: Our proposed evaluation procedure for peak picking of spatially structured peaks. (a) PCC values of all ion images and a corresponding binary mask are computed and sorted. (b) All ion images with a PCC value equal to or greater than $T_{PCC}$ for any annotated class are classified as positive. Steps (a) and (b) are repeated for every class. (c) We calculate F1-scores using thresholds $T_{PCC}\in\{0.3,0.4,0.5,0.6\}$ and report the mean of these four F1-scores, $mSCF1$ , as the final metric.

Dataset	Format	# $m/z$ values	# tissues	# classes	Ionization	Analyzer
Mouse glioblastoma [ 2 ]	Profile	85,062	8	2	MALDI	FTICR
Renal cell carcinoma [ 10 ]	Profile	10,200	8	3	DESI	Thermo LTQ Orbitrap
Colorectal adenocarcinoma [ 20 ]	Profile	1,481	8	3	DESI	Thermo Exactive
Gastrointestinal stromal tumor [ 4 ]	Profile	120,000	1	-	MALDI	TOF

Table 2: Overview of public dataset collections [ 2 , 10 , 20 , 4 ] used for the evaluation of spatially structured peak picking methods.

Dataset	Metric	msiPL [ 1 ]	Lieb et al. [ 24 ]	MALDIquant [ 16 ]	SPUTNIK [ 19 ]	S 3 PL (Ours)
GBM [ 2 ]	F1 0.3	45.6	0	29.3	50.8	56.4
	F1 0.4	44.4	0	26.8	43.0	55.6
	F1 0.5	39.4	0	23.6	34.4	48.8
	F1 0.6	31.9	0	18.5	23.8	37.1
	mSCF1	40.3	0	24.6	38.1	49.6
	95% CI of mSCF1	[33.0, 47.5]	-	[20.4, 28.9]	[28.9, 47.3]	[39.0, 60.2]

Table 3: Comparison of our spatial self-supervised peak learning autoencoder, S 3 PL, with state-of-the-art peak picking methods [ 16 , 24 , 19 , 1 ] using our proposed evaluation procedure. The table shows the F1-scores for

T_{PCC}\in\{0.3,0.4,0.5,0.6\}

and the mean of all four F1-scores,

mSCF1

. We average the results over all tissue sections in a dataset. Reproducibility Note: We introduce and optimize a threshold for Lieb et al.’s method, considering only m/z values that exceed the threshold as peaks, because this leads to more accurate results.

Figure 3: Detailed comparison of our spatial self-supervised peak learning autoencoder, S 3 PL, with state-of-the-art peak picking methods [ 16 , 24 , 19 , 1 ] using our proposed evaluation procedure. The figures show the

mSCF1

scores for each tissue section within each dataset separately.

Figure 4: Visualization of selected peaks by our S 3 PL method on the GIST dataset [ 4 ] in the representative mass range $m/z$ $563$ to $573$ . The depicted mean spectrum contains many distinct peaks, which do not necessarily correspond to spatially structured ion images. S 3 PL only selects peaks with spatial structure.

Figure 7: Ablation study on the best kernel depth $d_{1}$ and $d_{2}$ . We selected one tissue section from each of the three public datasets [ 2 , 10 , 20 ] . The heatmap shows the $mSCF1$ score for different combinations of kernel depths.

Figure 8: Variation of

mSCF1

scores with a fine-tuned peak number

n

on other tissue sections of a dataset collection [ 2 , 10 , 20 ] . The boxplots indicate the best and worst possible results when fine-tuning the number of peaks on one tissue section and using this number for every other tissue section of a dataset collection.

Self-Supervised Learning

Self-supervised learning uses structure within unlabeled data to train representations and models for downstream tasks.

See all 258 papers

IonMorphNet: Generalizable Learning of Ion Image Morphologies for Peak Picking in Mass Spectrometry Imaging
Apr 21, 2026Philipp Weigand, Niels Nawrot, Nikolas Ebert +2Representation Learning Mass Spectrometry
Mannheim University of Applied Sciences, Germany · Faculty of Biosciences, Heidelberg University, Germany
FlexMS: A Unified Public Benchmark for Molecule Tandem Mass Spectrum Prediction
Feb 26, 2026Yunhua Zhong, Yixuan Tang, Yifan Li +5Benchmark Design Mass Spectrometry
The Hong Kong University of Science and Technology (Guangzhou). · The University of Hong Kong. · Yangzhou University. +3
PepSpecBench: A Unified Evaluation Benchmark for Peptide Tandem Mass Spectrometry Prediction
May 3, 2026Zhiwen Yang, Pan Liu, Yifan Li +2Benchmark Design Benchmark Contamination
The Hong Kong University of Science and Technology (Guangzhou). · Yangzhou University. · The Hong Kong University of Science and Technology.

Apr 21, 2026J/K move · Enter open · S save

IonMorphNet: Generalizable Learning of Ion Image Morphologies for Peak Picking in Mass Spectrometry Imaging

Philipp Weigand, Niels Nawrot, Nikolas Ebert, Carsten Hopf+1

Mannheim University of Applied Sciences, Germany · Faculty of Biosciences, Heidelberg University, Germany

Representation Learning Mass Spectrometry

Open paper AskPDF arXiv

Peak picking is a fundamental preprocessing step in Mass Spectrometry Imaging (MSI), where each sample is represented by hundreds to thousands of ion images. Existing approaches require careful dataset-specific hyperparameter tuning, and often fail to generalize across acquisition protocols. We introduce IonMorphNet, a spatial-structure-aware representation model for ion images that enables fully data-driven peak picking without any task-specific supervision. We curate 53 publicly available MSI datasets and define six structural classes capturing representative spatial patterns in ion images to train standard image backbones for structural pattern classification. Once trained, IonMorphNet can assess ion images and perform peak picking without additional hyperparameter tuning. Using a ConvNeXt V2-Tiny backbone, our approach improves peak picking performance by +7 % mSCF1 compared to state-of-the-art methods across multiple datasets. Beyond peak picking, we demonstrate that spatially informed channel reduction enables a 3D CNN for patch-based tumor classification in MSI. This approach matches or exceeds pixel-wise spectral classifiers by up to +7.3 % Balanced Accuracy on three tumor classification tasks, indicating meaningful ion image selection. The source code and model weights are available at https://github.com/CeMOS-IS/IonMorphNet.

Abstract

Self-Supervised Learning

Explore similar work

IonMorphNet: Generalizable Learning of Ion Image Morphologies for Peak Picking in Mass Spectrometry Imaging

FlexMS: A Unified Public Benchmark for Molecule Tandem Mass Spectrum Prediction

PepSpecBench: A Unified Evaluation Benchmark for Peptide Tandem Mass Spectrometry Prediction