Multimodal Phasor Analysis for Digital Pathology: Quantitative Characterization of Liver Iron Overload

Hemochromatosis is a pathological condition characterized by the excessive absorption and storage of iron, primarily in the liver, which can lead to life-threatening complications. Histological tissue sections stained with Perls’ Prussian Blue (PB) are used to visualize iron accumulation under transmitted light. In clinical practice, pathologists evaluate the density and intensity of these Prussian Blue deposits through direct visual inspection, a semiquantitative process that can be influenced by individual interpretation and experience. Whole-slide scanners now digitize entire tissue sections into high-resolution RGB whole-slide images (WSIs), allowing image analysis algorithms to support pathologists with quantitative diagnostics. In this context, we developed a WSI processing method based on a multimodal phasor approach to quantify the extent, size and distribution of iron deposits, providing quantitative data that closely correlate with pathologists’ diagnoses based on the Scheuer scoring system. The Discrete Fourier Transform was used to map the RGB spectral data into the phasor plane, where the contribution of the PB stain was isolated using an unsupervised clustering method. Additionally, the size of Prussian Blue aggregates was determined through phasor analysis of the spatial autocorrelation function. Our results highlight the benefits of phasor-based analysis in segmentation, comprehensive analysis, and the efficient management of large image data sets. This method has potential applications in analyzing immunohistochemistry and immunofluorescence spectral images, as well as in simultaneously studying nanoparticle size and diffusion properties within tissues.

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Journal
Chemical & Biomedical Imaging
Published
2026-01-15
DOI
https://doi.org/10.1021/cbmi.5c00229
Citations
1
Primary Topic
AI in cancer detection
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article
Field-Weighted Citation Impact
52.10
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Multimodal Phasor Analysis for Digital Pathology: Quantitative Characterization of Liver Iron Overload

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Chemical & Biomedical Imaging
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Multimodal Phasor Analysis for Digital Pathology: Quantitative Characterization of Liver Iron Overload

Reha Akpınar, Luca Presotto, Maddalena Collini, Laura D’Alfonso, Donato Inverso, Giuseppe G. Chirico, Laura Sironi, Davide Panzeri, Margaux Bouzin
article en
1 citations

Abstract

Hemochromatosis is a pathological condition characterized by the excessive absorption and storage of iron, primarily in the liver, which can lead to life-threatening complications. Histological tissue sections stained with Perls’ Prussian Blue (PB) are used to visualize iron accumulation under transmitted light. In clinical practice, pathologists evaluate the density and intensity of these Prussian Blue deposits through direct visual inspection, a semiquantitative process that can be influenced by individual interpretation and experience. Whole-slide scanners now digitize entire tissue sections into high-resolution RGB whole-slide images (WSIs), allowing image analysis algorithms to support pathologists with quantitative diagnostics. In this context, we developed a WSI processing method based on a multimodal phasor approach to quantify the extent, size and distribution of iron deposits, providing quantitative data that closely correlate with pathologists’ diagnoses based on the Scheuer scoring system. The Discrete Fourier Transform was used to map the RGB spectral data into the phasor plane, where the contribution of the PB stain was isolated using an unsupervised clustering method. Additionally, the size of Prussian Blue aggregates was determined through phasor analysis of the spatial autocorrelation function. Our results highlight the benefits of phasor-based analysis in segmentation, comprehensive analysis, and the efficient management of large image data sets. This method has potential applications in analyzing immunohistochemistry and immunofluorescence spectral images, as well as in simultaneously studying nanoparticle size and diffusion properties within tissues.

Chemical & Biomedical Imaging
Humanitas University (IT), Vita-Salute San Raffaele University (IT), Institute of Transplantation Sciences (IN), Leibniz Institute for Analytical Sciences - ISAS (DE), Istituti di Ricovero e Cura a Carattere Scientifico (IT), IRCCS Humanitas Research Hospital (IT), Istituto di Ricovero e Cura a Carattere Scientifico San Raffaele (IT), University of Milano-Bicocca (IT)
Openalex Percentile: Top 1%
AI in cancer detection
52.10
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