IHC_Tool: An open-source Fiji procedure for quantitative evaluation of cross sections of testicular explants

Dumont, Ludovic; Levacher, Nicolas; Schapman, Damien; Rives-Feraille, Aurélie; Moutard, Laura; Delessard, Marion; Saulnier, Justine; Rondanino, Christine; Rives, Nathalie

doi:10.1016/j.repbio.2021.100507

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…A single cross-section of a mouse testis contains ∼120 tubules that house tens of thousands of germ cells discriminated by histological markers into 20 cell types ( Chiarini-Garcia and Meistrich, 2008 ; Drumond et al , 2011 ) and 12 tubule stages ( Ahmed and de Rooij, 2009 ; Oakberg, 1956 ; Russell et al , 1990 ) that serve as landmarks in the cycle of spermatogenesis [reviewed in Lara et al (2018) ]. While the quality and quantity of testis histology has greatly improved over the last decade, computational tools capable of handling and analyzing such data are just emerging ( Bell et al , 2020 ; Dumont et al , 2021 ; Liang et al , 2022 ; Sziva et al , 2022 ; Xu et al , 2021 ).…”

Section: Introductionmentioning

confidence: 99%

SATINN: an automated neural network-based classification of testicular sections allows for high-throughput histopathology of mouse mutants

et al. 2022

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Motivation The mammalian testis is a complex organ with a cellular composition that changes smoothly and cyclically in normal adults. While testis histology is already an invaluable tool for identifying and describing developmental differences in evolution and disease, methods for standardized, digital image analysis of testis are needed to expand the utility of this approach. Results We developed SATINN (Software for Analysis of Testis Images with Neural Networks), a multi-level framework for automated analysis of multiplexed immunofluorescence images from mouse testis. This approach uses residual learning to train convolutional neural networks (CNNs) to classify nuclei from seminiferous tubules into 7 distinct cell types with an accuracy of 81.7%. These cell classifications are then used in a second-level tubule CNN, which places seminiferous tubules into one of 12 distinct tubule stages with 57.3% direct accuracy and 94.9% within ±1 stage. We further describe numerous cell- and tubule-level statistics that can be derived from wildtype testis. Finally, we demonstrate how the classifiers and derived statistics can be used to rapidly and precisely describe pathology by applying our methods to image data from two mutant mouse lines. Our results demonstrate the feasibility and potential of using computer-assisted analysis for testis histology, an area poised to evolve rapidly on the back of emerging, spatially-resolved genomic and proteomic technologies. Availability The source code to reproduce the results described here and a SATINN standalone application with graphic-user interface are available from http://github.com/conradlab/SATINN. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 99%

SATINN: an automated neural network-based classification of testicular sections allows for high-throughput histopathology of mouse mutants

et al. 2022

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“…According to previously well-described digital histomorphometrical measurement methods reported by former researchers, we planned and implemented a complex and detailed methodology: we determined multiple measurable and calculable parameters not only on the tubular level but on the whole testis cross-sectional level at the same time. Dumont L et al, based on the microscope-coupled image-making method, has already developed an automated digital immunohistochemistry image analysis tool to reduce the background and noise, to generate stitched high-resolution images of testis sections and to extract the necrotic area and to count the immunostained cells [61].…”

Section: Discussionmentioning

confidence: 99%

Accurate Quantitative Histomorphometric-Mathematical Image Analysis Methodology of Rodent Testicular Tissue and Its Possible Future Research Perspectives in Andrology and Reproductive Medicine

Sziva

Ács

Tőkés

et al. 2022

Life

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Infertility is increasing worldwide; male factors can be identified in nearly half of all infertile couples. Histopathologic evaluation of testicular tissue can provide valuable information about infertility; however, several different evaluation methods and semi-quantitative score systems exist. Our goal was to describe a new, accurate and easy-to-use quantitative computer-based histomorphometric-mathematical image analysis methodology for the analysis of testicular tissue. On digitized, original hematoxylin-eosin (HE)-stained slides (scanned by slide-scanner), quantitatively describable characteristics such as area, perimeter and diameter of testis cross-sections and of individual tubules were measured with the help of continuous magnification. Immunohistochemically (IHC)-stained slides were digitized with a microscope-coupled camera, and IHC-staining intensity measurements on digitized images were also taken. Suggested methods are presented with mathematical equations, step-by-step detailed characterization and representative images are given. Our novel quantitative histomorphometric-mathematical image analysis method can improve the reproducibility, objectivity, quality and comparability of andrological-reproductive medicine research by recognizing even the mild impairments of the testicular structure expressed numerically, which might not be detected with the present semi-quantitative score systems. The technique is apt to be subjected to further automation with machine learning and artificial intelligence and can be named ‘Computer-Assisted or -Aided Testis Histology’ (CATHI).

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“…The article in [ 17 ] was devoted to the software tool for analyzing IHC images for the quantitative assessment of tissue pathology. The authors developed an automated IHC_Tool procedure with TIFF images at a magnification of _200 to quantify the cell traits.…”

Section: Literature Reviewmentioning

confidence: 99%

An Approach toward Automatic Specifics Diagnosis of Breast Cancer Based on an Immunohistochemical Image

Berezsky

Pitsun²,

Melnyk³

et al. 2023

J. Imaging

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The paper explored the problem of automatic diagnosis based on immunohistochemical image analysis. The issue of automated diagnosis is a preliminary and advisory statement for a diagnostician. The authors studied breast cancer histological and immunohistochemical images using the following biomarkers progesterone, estrogen, oncoprotein, and a cell proliferation biomarker. The authors developed a breast cancer diagnosis method based on immunohistochemical image analysis. The proposed method consists of algorithms for image preprocessing, segmentation, and the determination of informative indicators (relative area and intensity of cells) and an algorithm for determining the molecular genetic breast cancer subtype. An adaptive algorithm for image preprocessing was developed to improve the quality of the images. It includes median filtering and image brightness equalization techniques. In addition, the authors developed a software module part of the HIAMS software package based on the Java programming language and the OpenCV computer vision library. Four molecular genetic breast cancer subtypes could be identified using this solution: subtype Luminal A, subtype Luminal B, subtype HER2/neu amplified, and basalt-like subtype. The developed algorithm for the quantitative characteristics of the immunohistochemical images showed sufficient accuracy in determining the cancer subtype “Luminal A”. It was experimentally established that the relative area of the nuclei of cells covered with biomarkers of progesterone, estrogen, and oncoprotein was more than 85%. The given approach allows for automating and accelerating the process of diagnosis. Developed algorithms for calculating the quantitative characteristics of cells on immunohistochemical images can increase the accuracy of diagnosis.

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IHC_Tool: An open-source Fiji procedure for quantitative evaluation of cross sections of testicular explants

Cited by 6 publications

References 29 publications

SATINN: an automated neural network-based classification of testicular sections allows for high-throughput histopathology of mouse mutants

SATINN: an automated neural network-based classification of testicular sections allows for high-throughput histopathology of mouse mutants

Accurate Quantitative Histomorphometric-Mathematical Image Analysis Methodology of Rodent Testicular Tissue and Its Possible Future Research Perspectives in Andrology and Reproductive Medicine

An Approach toward Automatic Specifics Diagnosis of Breast Cancer Based on an Immunohistochemical Image

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