A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation.

Breast / diagnostic imaging Breast Density Breast Neoplasms / diagnostic imaging Deep Learning Female Humans Image Processing, Computer-Assisted Mammography
Breast density Deep learning Dense tissue segmentation Entirely convolutional neural network (ECNN) Mammography

Journal

Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513

Informations de publication

Date de publication:
Oct 2020
Historique:
received: 06 02 2020
accepted: 13 07 2020
pubmed: 7 8 2020
medline: 15 5 2021
entrez: 7 8 2020
Statut: ppublish

Résumé

Breast cancer is the most frequent cancer in women. The Spanish healthcare network established population-based screening programs in all Autonomous Communities, where mammograms of asymptomatic women are taken with early diagnosis purposes. Breast density assessed from digital mammograms is a biomarker known to be related to a higher risk to develop breast cancer.It is thus crucial to provide a reliable method to measure breast density from mammograms. Furthermore the complete automation of this segmentation process is becoming fundamental as the amount of mammograms increases every day. Important challenges are related with the differences in images from different devices and the lack of an objective gold standard.This paper presents a fully automated framework based on deep learning to estimate the breast density. The framework covers breast detection, pectoral muscle exclusion, and fibroglandular tissue segmentation. A multi-center study, composed of 1785 women whose "for presentation" mammograms were segmented by two experienced radiologists. A total of 4992 of the 6680 mammograms were used as training corpus and the remaining (1688) formed the test corpus. This paper presents a histogram normalization step that smoothed the difference between acquisition, a regression architecture that learned segmentation parameters as intrinsic image features and a loss function based on the DICE score. The results obtained indicate that the level of concordance (DICE score) reached by the two radiologists (0.77) was also achieved by the automated framework when it was compared to the closest breast segmentation from the radiologists. For the acquired with the highest quality device, the DICE score per acquisition device reached 0.84, while the concordance between radiologists was 0.76. An automatic breast density estimator based on deep learning exhibits similar performance when compared with two experienced radiologists. It suggests that this system could be used to support radiologists to ease its work.

Sections du résumé

BACKGROUND AND OBJECTIVE OBJECTIVE
Breast cancer is the most frequent cancer in women. The Spanish healthcare network established population-based screening programs in all Autonomous Communities, where mammograms of asymptomatic women are taken with early diagnosis purposes. Breast density assessed from digital mammograms is a biomarker known to be related to a higher risk to develop breast cancer.It is thus crucial to provide a reliable method to measure breast density from mammograms. Furthermore the complete automation of this segmentation process is becoming fundamental as the amount of mammograms increases every day. Important challenges are related with the differences in images from different devices and the lack of an objective gold standard.This paper presents a fully automated framework based on deep learning to estimate the breast density. The framework covers breast detection, pectoral muscle exclusion, and fibroglandular tissue segmentation.
METHODS METHODS
A multi-center study, composed of 1785 women whose "for presentation" mammograms were segmented by two experienced radiologists. A total of 4992 of the 6680 mammograms were used as training corpus and the remaining (1688) formed the test corpus. This paper presents a histogram normalization step that smoothed the difference between acquisition, a regression architecture that learned segmentation parameters as intrinsic image features and a loss function based on the DICE score.
RESULTS RESULTS
The results obtained indicate that the level of concordance (DICE score) reached by the two radiologists (0.77) was also achieved by the automated framework when it was compared to the closest breast segmentation from the radiologists. For the acquired with the highest quality device, the DICE score per acquisition device reached 0.84, while the concordance between radiologists was 0.76.
CONCLUSIONS CONCLUSIONS
An automatic breast density estimator based on deep learning exhibits similar performance when compared with two experienced radiologists. It suggests that this system could be used to support radiologists to ease its work.

Identifiants

DOI: 10.1016/j.cmpb.2020.105668 PMID: 32755754
pubmed: 32755754
pii: S0169-2607(20)31501-7
doi: 10.1016/j.cmpb.2020.105668
pii:
doi:

Types de publication

Journal Article Multicenter Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

105668

Informations de copyright

Copyright © 2020 Elsevier B.V. All rights reserved.

Auteurs

Francisco Javier Pérez-Benito (FJ)

Instituto Tecnológico de la Informática, Universitat Politècnica de València, Camino de Vera, s/n, València 46022, Spain. Electronic address: fjperez@iti.es.

François Signol (F)

Instituto Tecnológico de la Informática, Universitat Politècnica de València, Camino de Vera, s/n, València 46022, Spain. Electronic address: fsignol@iti.es.

Juan-Carlos Perez-Cortes (JC)

Instituto Tecnológico de la Informática, Universitat Politècnica de València, Camino de Vera, s/n, València 46022, Spain. Electronic address: jcperez@iti.upv.es.

Alejandro Fuster-Baggetto (A)

Instituto Tecnológico de la Informática, Universitat Politècnica de València, Camino de Vera, s/n, València 46022, Spain. Electronic address: afuster@iti.es.

Marina Pollan (M)

National Center for Epidemiology, Carlos III Institute of Health, Monforte de lemos 5, Madrid 28029, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Carlos III Institute of Health, Monforte de Lemos 5, Madrid 28029, Spain. Electronic address: mpollan@isciii.es.

Beatriz Pérez-Gómez (B)

National Center for Epidemiology, Carlos III Institute of Health, Monforte de lemos 5, Madrid 28029, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Carlos III Institute of Health, Monforte de Lemos 5, Madrid 28029, Spain. Electronic address: bperez@isciii.es.

Dolores Salas-Trejo (D)

Valencian Breast Cancer Screening Program, General Directorate of Public Health, València, Spain; Centro Superior de Investigación en Salud Pública CSISP, FISABIO, València, Spain. Electronic address: salasdol@gva.es.

Maria Casals (M)

Valencian Breast Cancer Screening Program, General Directorate of Public Health, València, Spain; Centro Superior de Investigación en Salud Pública CSISP, FISABIO, València, Spain. Electronic address: casalsmar@gva.es.

Inmaculada Martínez (I)

Valencian Breast Cancer Screening Program, General Directorate of Public Health, València, Spain; Centro Superior de Investigación en Salud Pública CSISP, FISABIO, València, Spain. Electronic address: martinezinm@gva.es.

Rafael LLobet (R)

Instituto Tecnológico de la Informática, Universitat Politècnica de València, Camino de Vera, s/n, València 46022, Spain. Electronic address: rllobet@iti.upv.es.

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Classifications MeSH

questionsmedicales.fr › Régions du corps › Région mammaire › A deep learning system to obtain the optimal...
questionsmedicales.fr › Phénomènes physiologiques › Constitution physique › Densité mammaire › A deep learning system to obtain the optimal...
questionsmedicales.fr › Maladies de la peau et du tissu conjonctif › Maladies de la peau › Maladies du sein › Tumeurs du sein › A deep learning system to obtain the optimal...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Réseaux neuronaux (ordinateur) › Apprentissage profond › A deep learning system to obtain the optimal...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › A deep learning system to obtain the optimal...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Traitement d'image par ordinateur › A deep learning system to obtain the optimal...
questionsmedicales.fr › Diagnostic › Techniques et procédures diagnostiques › Imagerie diagnostique › Radiographie › Mammographie › A deep learning system to obtain the optimal...