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Article

Deep Learning Based Segmentation of Breast Lesions in DCE-MRI

Authors:

Roa’a Khaled,

Robert MartíAuthors Info & Claims

Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10–15, 2021, Proceedings, Part I

Pages 417 - 430

https://doi.org/10.1007/978-3-030-68763-2_32

Published: 10 January 2021 Publication History

Abstract

Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is a popular tool for the diagnosis of breast lesions due to its effectiveness, especially in a high risk population. Accurate lesion segmentation is an important step for subsequent analysis, especially for computer aided diagnosis systems. However, manual breast lesion segmentation of (4D) MRI is time consuming, requires experience, and it is prone to interobserver and intraobserver variability. This work proposes a deep learning (DL) framework for segmenting breast lesions in DCE-MRI using a 3D patch based U-Net architecture. We perform different experiments to analyse the effects of class imbalance, different patch sizes, optimizers and loss functions in a cross-validation fashion using 46 images from a subset of a challenging and publicly available dataset not reported to date, that is the TCGA-BRCA. We also compare the proposed U-Net framework with another state-of-the-art approach used for breast lesion segmentation in DCE-MRI, and report better segmentation accuracy with the proposed framework. The results presented in this work have the potential to become a publicly available benchmark for this task.

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Cited By

Lv TLiu YMiao KLi LPan X(2023)Diffusion Kinetic Model for Breast Cancer Segmentation in Incomplete DCE-MRIMedical Image Computing and Computer Assisted Intervention – MICCAI 202310.1007/978-3-031-43901-8_10(100-109)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-43901-8_10
Khaled RVidal JVilanova JMartí R(2022)A U-Net Ensemble for breast lesion segmentation in DCE MRIComputers in Biology and Medicine10.1016/j.compbiomed.2021.105093140:COnline publication date: 6-May-2022
https://dl.acm.org/doi/10.1016/j.compbiomed.2021.105093

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Information & Contributors

Information

Published In

cover image Guide Proceedings

Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10–15, 2021, Proceedings, Part I

Jan 2021

756 pages

ISBN:978-3-030-68762-5

DOI:10.1007/978-3-030-68763-2

Editors:
Alberto Del Bimbo
Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy
,
Rita Cucchiara
Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy
,
Stan Sclaroff
Department of Computer Science, Boston University, Boston, MA, USA
,
Giovanni Maria Farinella
Dipartimento di Matematica e Informatica, University of Catania, Catania, Italy
,
Tao Mei
Cloud & AI, JD.COM, Beijing, China
,
Marco Bertini
Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy
,
Hugo Jair Escalante
Computational Sciences Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla, Mexico
,
Roberto Vezzani
Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

© Springer Nature Switzerland AG 2021.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 10 January 2021

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Cited By

Lv TLiu YMiao KLi LPan X(2023)Diffusion Kinetic Model for Breast Cancer Segmentation in Incomplete DCE-MRIMedical Image Computing and Computer Assisted Intervention – MICCAI 202310.1007/978-3-031-43901-8_10(100-109)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-43901-8_10
Khaled RVidal JVilanova JMartí R(2022)A U-Net Ensemble for breast lesion segmentation in DCE MRIComputers in Biology and Medicine10.1016/j.compbiomed.2021.105093140:COnline publication date: 6-May-2022
https://dl.acm.org/doi/10.1016/j.compbiomed.2021.105093

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