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Updated: Aug 12, 2025

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

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Published on: October 28, 2018

Next-Generation Morphometry for pathomics-data mining in histopathology.

David L Hölscher¹, Nassim Bouteldja¹, Mehdi Joodaki²

¹Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany.

Nature Communications

|January 28, 2023

Summary

This summary is machine-generated.

We developed a framework for large-scale histomorphometry (FLASH) using deep learning to extract quantitative features from kidney histology. These features predict clinical outcomes and reveal disease progression patterns, enabling next-generation morphometry (NGM) for pathomics.

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Area of Science:

Computational pathology
Digital pathology
Nephrology

Background:

Pathology diagnostics heavily rely on subjective, qualitative morphological assessment by experts.
Current methods lack quantitative, large-scale analysis of histological features.

Purpose of the Study:

To develop a deep learning framework for quantitative histomorphometry in kidney pathology.
To extract interpretable, quantitative morphometric features from non-tumour kidney histology.
To establish a concept for Next-generation Morphometry (NGM) for comprehensive pathomics.

Main Methods:

Developed a framework for large-scale histomorphometry (FLASH) using deep learning-based semantic segmentation.
Applied FLASH to over 1000 kidney biopsies and nephrectomies across multiple cohorts.
Utilized single-structure morphometric analysis techniques inspired by single-cell transcriptomics.

Main Results:

Extracted interpretable, quantitative morphometric features from kidney histology.
Associated morphometric features with clinical parameters, confirming known and revealing novel relationships.
Demonstrated that extracted features are independent predictors of long-term clinical outcomes in IgA-nephropathy.
Identified distinct glomerular populations and morphometric phenotypes along disease progression trajectories.

Conclusions:

FLASH provides a robust method for quantitative analysis in kidney pathology.
NGM enables comprehensive quantitative pathology data mining (pathomics).
Quantitative morphometric features offer valuable insights into disease mechanisms and clinical outcomes.