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

Journal: Computer methods and programs in biomedicine
Published Date: Jul 24, 2020

Abstract

BACKGROUND AND 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.

Authors

  • Francisco Javier Pérez-Benito
    Biomedical Data Science Lab. Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politécnica de Valéncia, Camino de Vera s/n, Valencia 46022, Spain. Electronic address: frapebe@doctor.upv.es.
  • François Signol
    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
    Instituto Tecnológico de Informática (ITI), Universitat Politècnica de València, 46022 Valencia, Spain.
  • Alejandro Fuster-Baggetto
    Instituto Tecnológico de la Informática, Universitat Politècnica de València, Camino de Vera, s/n, València 46022, Spain. Electronic address: afuster@iti.es.
  • Marina Pollan
    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
    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
    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
    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
    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
    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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