Cargando…
Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation
Liver transplantation is the only curative treatment option in patients diagnosed with end-stage liver disease. The low availability of organs demands an accurate selection procedure based on histological analysis, in order to evaluate the allograft. This assessment, traditionally carried out by a p...
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8001362/ https://www.ncbi.nlm.nih.gov/pubmed/33808978 http://dx.doi.org/10.3390/s21061993 |
_version_ | 1783671212091637760 |
---|---|
author | Pérez-Sanz, Fernando Riquelme-Pérez, Miriam Martínez-Barba, Enrique de la Peña-Moral, Jesús Salazar Nicolás, Alejandro Carpes-Ruiz, Marina Esteban-Gil, Angel Legaz-García, María Del Carmen Parreño-González, María Antonia Ramírez, Pablo Martínez, Carlos M. |
author_facet | Pérez-Sanz, Fernando Riquelme-Pérez, Miriam Martínez-Barba, Enrique de la Peña-Moral, Jesús Salazar Nicolás, Alejandro Carpes-Ruiz, Marina Esteban-Gil, Angel Legaz-García, María Del Carmen Parreño-González, María Antonia Ramírez, Pablo Martínez, Carlos M. |
author_sort | Pérez-Sanz, Fernando |
collection | PubMed |
description | Liver transplantation is the only curative treatment option in patients diagnosed with end-stage liver disease. The low availability of organs demands an accurate selection procedure based on histological analysis, in order to evaluate the allograft. This assessment, traditionally carried out by a pathologist, is not exempt from subjectivity. In this sense, new tools based on machine learning and artificial vision are continuously being developed for the analysis of medical images of different typologies. Accordingly, in this work, we develop a computer vision-based application for the fast and automatic objective quantification of macrovesicular steatosis in histopathological liver section slides stained with Sudan stain. For this purpose, digital microscopy images were used to obtain thousands of feature vectors based on the RGB and CIE L*a*b* pixel values. These vectors, under a supervised process, were labelled as fat vacuole or non-fat vacuole, and a set of classifiers based on different algorithms were trained, accordingly. The results obtained showed an overall high accuracy for all classifiers (>0.99) with a sensitivity between 0.844 and 1, together with a specificity >0.99. In relation to their speed when classifying images, KNN and Naïve Bayes were substantially faster than other classification algorithms. Sudan stain is a convenient technique for evaluating ME in pre-transplant liver biopsies, providing reliable contrast and facilitating fast and accurate quantification through the machine learning algorithms tested. |
format | Online Article Text |
id | pubmed-8001362 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80013622021-03-28 Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation Pérez-Sanz, Fernando Riquelme-Pérez, Miriam Martínez-Barba, Enrique de la Peña-Moral, Jesús Salazar Nicolás, Alejandro Carpes-Ruiz, Marina Esteban-Gil, Angel Legaz-García, María Del Carmen Parreño-González, María Antonia Ramírez, Pablo Martínez, Carlos M. Sensors (Basel) Article Liver transplantation is the only curative treatment option in patients diagnosed with end-stage liver disease. The low availability of organs demands an accurate selection procedure based on histological analysis, in order to evaluate the allograft. This assessment, traditionally carried out by a pathologist, is not exempt from subjectivity. In this sense, new tools based on machine learning and artificial vision are continuously being developed for the analysis of medical images of different typologies. Accordingly, in this work, we develop a computer vision-based application for the fast and automatic objective quantification of macrovesicular steatosis in histopathological liver section slides stained with Sudan stain. For this purpose, digital microscopy images were used to obtain thousands of feature vectors based on the RGB and CIE L*a*b* pixel values. These vectors, under a supervised process, were labelled as fat vacuole or non-fat vacuole, and a set of classifiers based on different algorithms were trained, accordingly. The results obtained showed an overall high accuracy for all classifiers (>0.99) with a sensitivity between 0.844 and 1, together with a specificity >0.99. In relation to their speed when classifying images, KNN and Naïve Bayes were substantially faster than other classification algorithms. Sudan stain is a convenient technique for evaluating ME in pre-transplant liver biopsies, providing reliable contrast and facilitating fast and accurate quantification through the machine learning algorithms tested. MDPI 2021-03-12 /pmc/articles/PMC8001362/ /pubmed/33808978 http://dx.doi.org/10.3390/s21061993 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pérez-Sanz, Fernando Riquelme-Pérez, Miriam Martínez-Barba, Enrique de la Peña-Moral, Jesús Salazar Nicolás, Alejandro Carpes-Ruiz, Marina Esteban-Gil, Angel Legaz-García, María Del Carmen Parreño-González, María Antonia Ramírez, Pablo Martínez, Carlos M. Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation |
title | Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation |
title_full | Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation |
title_fullStr | Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation |
title_full_unstemmed | Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation |
title_short | Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation |
title_sort | efficiency of machine learning algorithms for the determination of macrovesicular steatosis in frozen sections stained with sudan to evaluate the quality of the graft in liver transplantation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8001362/ https://www.ncbi.nlm.nih.gov/pubmed/33808978 http://dx.doi.org/10.3390/s21061993 |
work_keys_str_mv | AT perezsanzfernando efficiencyofmachinelearningalgorithmsforthedeterminationofmacrovesicularsteatosisinfrozensectionsstainedwithsudantoevaluatethequalityofthegraftinlivertransplantation AT riquelmeperezmiriam efficiencyofmachinelearningalgorithmsforthedeterminationofmacrovesicularsteatosisinfrozensectionsstainedwithsudantoevaluatethequalityofthegraftinlivertransplantation AT martinezbarbaenrique efficiencyofmachinelearningalgorithmsforthedeterminationofmacrovesicularsteatosisinfrozensectionsstainedwithsudantoevaluatethequalityofthegraftinlivertransplantation AT delapenamoraljesus efficiencyofmachinelearningalgorithmsforthedeterminationofmacrovesicularsteatosisinfrozensectionsstainedwithsudantoevaluatethequalityofthegraftinlivertransplantation AT salazarnicolasalejandro efficiencyofmachinelearningalgorithmsforthedeterminationofmacrovesicularsteatosisinfrozensectionsstainedwithsudantoevaluatethequalityofthegraftinlivertransplantation AT carpesruizmarina efficiencyofmachinelearningalgorithmsforthedeterminationofmacrovesicularsteatosisinfrozensectionsstainedwithsudantoevaluatethequalityofthegraftinlivertransplantation AT estebangilangel efficiencyofmachinelearningalgorithmsforthedeterminationofmacrovesicularsteatosisinfrozensectionsstainedwithsudantoevaluatethequalityofthegraftinlivertransplantation AT legazgarciamariadelcarmen efficiencyofmachinelearningalgorithmsforthedeterminationofmacrovesicularsteatosisinfrozensectionsstainedwithsudantoevaluatethequalityofthegraftinlivertransplantation AT parrenogonzalezmariaantonia efficiencyofmachinelearningalgorithmsforthedeterminationofmacrovesicularsteatosisinfrozensectionsstainedwithsudantoevaluatethequalityofthegraftinlivertransplantation AT ramirezpablo efficiencyofmachinelearningalgorithmsforthedeterminationofmacrovesicularsteatosisinfrozensectionsstainedwithsudantoevaluatethequalityofthegraftinlivertransplantation AT martinezcarlosm efficiencyofmachinelearningalgorithmsforthedeterminationofmacrovesicularsteatosisinfrozensectionsstainedwithsudantoevaluatethequalityofthegraftinlivertransplantation |