A neural network for glomerulus classification based on histological images of kidney biopsy

Abstract Background Computer-aided diagnosis (CAD) systems based on medical images could support physicians in the decision-making process. During the last decades, researchers have proposed CAD systems in several medical domains achieving promising results. CAD systems play an important role in dig...

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Autores principales:	Giacomo Donato Cascarano, Francesco Saverio Debitonto, Ruggero Lemma, Antonio Brunetti, Domenico Buongiorno, Irio De Feudis, Andrea Guerriero, Umberto Venere, Silvia Matino, Maria Teresa Rocchetti, Michele Rossini, Francesco Pesce, Loreto Gesualdo, Vitoantonio Bevilacqua
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	CKD Kidney Glomerulus classification Morphological features Texture features ANN Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/db873284506541638164c996738de283
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spelling	oai:doaj.org-article:db873284506541638164c996738de2832021-11-08T10:59:21ZA neural network for glomerulus classification based on histological images of kidney biopsy10.1186/s12911-021-01650-31472-6947https://doaj.org/article/db873284506541638164c996738de2832021-11-01T00:00:00Zhttps://doi.org/10.1186/s12911-021-01650-3https://doaj.org/toc/1472-6947Abstract Background Computer-aided diagnosis (CAD) systems based on medical images could support physicians in the decision-making process. During the last decades, researchers have proposed CAD systems in several medical domains achieving promising results. CAD systems play an important role in digital pathology supporting pathologists in analyzing biopsy slides by means of standardized and objective workflows. In the proposed work, we designed and tested a novel CAD system module based on image processing techniques and machine learning, whose objective was to classify the condition affecting renal corpuscles (glomeruli) between sclerotic and non-sclerotic. Such discrimination is useful for the biopsy slides evaluation performed by pathologists. Results We collected 26 digital slides taken from the kidneys of 19 donors with Periodic Acid-Schiff staining. Expert pathologists have conducted the slides preparation, digital acquisition and glomeruli annotations. Before setting the classifiers, we evaluated several feature extraction techniques from the annotated regions. Then, a feature reduction procedure followed by a shallow artificial neural network allowed discriminating between the glomeruli classes. We evaluated the workflow considering an independent dataset (i.e., processing images not used in the training procedure). Ten independent runs of the training algorithm, and evaluation, allowed achieving MCC and Accuracy of 0.95 (± 0.01) and 0.99 (standard deviation < 0.00), respectively. We also obtained good precision (0.9844 ± 0.0111) and recall (0.9310 ± 0.0153). Conclusions Results on the test set confirm that the proposed workflow is consistent and reliable for the investigated domain, and it can support the clinical practice of discriminating the two classes of glomeruli. Analyses on misclassifications show that the involved images are usually affected by staining artefacts or present partial sections due to slice preparation and staining processes. In clinical practice, however, pathologists discard images showing such artefacts.Giacomo Donato CascaranoFrancesco Saverio DebitontoRuggero LemmaAntonio BrunettiDomenico BuongiornoIrio De FeudisAndrea GuerrieroUmberto VenereSilvia MatinoMaria Teresa RocchettiMichele RossiniFrancesco PesceLoreto GesualdoVitoantonio BevilacquaBMCarticleCKDKidneyGlomerulus classificationMorphological featuresTexture featuresANNComputer applications to medicine. Medical informaticsR858-859.7ENBMC Medical Informatics and Decision Making, Vol 21, Iss S1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	CKD Kidney Glomerulus classification Morphological features Texture features ANN Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	CKD Kidney Glomerulus classification Morphological features Texture features ANN Computer applications to medicine. Medical informatics R858-859.7 Giacomo Donato Cascarano Francesco Saverio Debitonto Ruggero Lemma Antonio Brunetti Domenico Buongiorno Irio De Feudis Andrea Guerriero Umberto Venere Silvia Matino Maria Teresa Rocchetti Michele Rossini Francesco Pesce Loreto Gesualdo Vitoantonio Bevilacqua A neural network for glomerulus classification based on histological images of kidney biopsy
description	Abstract Background Computer-aided diagnosis (CAD) systems based on medical images could support physicians in the decision-making process. During the last decades, researchers have proposed CAD systems in several medical domains achieving promising results. CAD systems play an important role in digital pathology supporting pathologists in analyzing biopsy slides by means of standardized and objective workflows. In the proposed work, we designed and tested a novel CAD system module based on image processing techniques and machine learning, whose objective was to classify the condition affecting renal corpuscles (glomeruli) between sclerotic and non-sclerotic. Such discrimination is useful for the biopsy slides evaluation performed by pathologists. Results We collected 26 digital slides taken from the kidneys of 19 donors with Periodic Acid-Schiff staining. Expert pathologists have conducted the slides preparation, digital acquisition and glomeruli annotations. Before setting the classifiers, we evaluated several feature extraction techniques from the annotated regions. Then, a feature reduction procedure followed by a shallow artificial neural network allowed discriminating between the glomeruli classes. We evaluated the workflow considering an independent dataset (i.e., processing images not used in the training procedure). Ten independent runs of the training algorithm, and evaluation, allowed achieving MCC and Accuracy of 0.95 (± 0.01) and 0.99 (standard deviation < 0.00), respectively. We also obtained good precision (0.9844 ± 0.0111) and recall (0.9310 ± 0.0153). Conclusions Results on the test set confirm that the proposed workflow is consistent and reliable for the investigated domain, and it can support the clinical practice of discriminating the two classes of glomeruli. Analyses on misclassifications show that the involved images are usually affected by staining artefacts or present partial sections due to slice preparation and staining processes. In clinical practice, however, pathologists discard images showing such artefacts.
format	article
author	Giacomo Donato Cascarano Francesco Saverio Debitonto Ruggero Lemma Antonio Brunetti Domenico Buongiorno Irio De Feudis Andrea Guerriero Umberto Venere Silvia Matino Maria Teresa Rocchetti Michele Rossini Francesco Pesce Loreto Gesualdo Vitoantonio Bevilacqua
author_facet	Giacomo Donato Cascarano Francesco Saverio Debitonto Ruggero Lemma Antonio Brunetti Domenico Buongiorno Irio De Feudis Andrea Guerriero Umberto Venere Silvia Matino Maria Teresa Rocchetti Michele Rossini Francesco Pesce Loreto Gesualdo Vitoantonio Bevilacqua
author_sort	Giacomo Donato Cascarano
title	A neural network for glomerulus classification based on histological images of kidney biopsy
title_short	A neural network for glomerulus classification based on histological images of kidney biopsy
title_full	A neural network for glomerulus classification based on histological images of kidney biopsy
title_fullStr	A neural network for glomerulus classification based on histological images of kidney biopsy
title_full_unstemmed	A neural network for glomerulus classification based on histological images of kidney biopsy
title_sort	neural network for glomerulus classification based on histological images of kidney biopsy
publisher	BMC
publishDate	2021
url	https://doaj.org/article/db873284506541638164c996738de283
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A neural network for glomerulus classification based on histological images of kidney biopsy

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