A U-Net Ensemble for breast lesion segmentation in DCE MRI
dc.contributor.author
dc.date.accessioned
2022-01-24T10:29:51Z
dc.date.available
2022-01-24T10:29:51Z
dc.date.issued
2022-01-01
dc.identifier.issn
0010-4825
dc.identifier.uri
dc.description.abstract
Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) has been recognized as an effective tool for Breast Cancer (BC) diagnosis. Automatic BC analysis from DCE-MRI depends on features extracted particularly from lesions, hence, lesions need to be accurately segmented as a prior step. Due to the time and experience required to manually segment lesions in 4D DCE-MRI, automating this task is expected to reduce the workload, reduce observer variability and improve diagnostic accuracy.
In this paper we propose an automated method for breast lesion segmentation from DCE-MRI based on a U-Net framework. The contributions of this work are the proposal of a modified U-Net architecture and the analysis of the input DCE information. In that sense, we propose the use of an ensemble method combining three U-Net models, each using a different input combination, outperforming all individual methods and other existing approaches.
For evaluation, we use a subset of 46 cases from the TCGA-BRCA dataset, a challenging and publicly available dataset not reported to date for this task. Due to the incomplete annotations provided, we complement them with the help of a radiologist in order to include secondary lesions that were not originally segmented. The proposed ensemble method obtains a mean Dice Similarity Coefficient (DSC) of 0.680 (0.802 for main lesions) which outperforms state-of-the art methods using the same dataset, demonstrating the effectiveness of our method considering the complexity of the dataset
dc.description.sponsorship
This work was partially supported by the project ICEBERG: Image Computing for Enhancing Breast Cancer Radiomics (RTI2018-096 333-B-I00, Spanish Ministry)
Open Access funding provided thanks to the CRUE-CSIC agreement with Elsevier
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.relation.isformatof
Reproducció digital del document publicat a: https://doi.org/10.1016/j.compbiomed.2021.105093
dc.relation.ispartof
Computers in Biology and Medicine, 2021, vol. 140, art.núm. 105093
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Articles publicats (D-ATC)
dc.rights
Attribution 4.0 International
dc.rights.uri
dc.subject
dc.title
A U-Net Ensemble for breast lesion segmentation in DCE MRI
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.identifier.idgrec
035249
dc.type.peerreviewed
peer-reviewed
dc.identifier.eissn
1879-0534