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DC Field | Value | Language |
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dc.contributor.author | Berchiolli, M | - |
dc.contributor.author | Wolfram, S | - |
dc.contributor.author | Balachandran, W | - |
dc.contributor.author | Gan, T-H | - |
dc.date.accessioned | 2023-11-06T15:36:43Z | - |
dc.date.available | 2023-09-09 | - |
dc.date.available | 2023-11-06T15:36:43Z | - |
dc.date.issued | 2023-09-09 | - |
dc.identifier | ORCID iD: Wamadeva Balachandran https://orcid.org/0000-0002-4806-2257 | - |
dc.identifier | ORCID iD: Tat-Hean Gan https://orcid.org/0000-0002-5598-8453 | - |
dc.identifier | 10160 | - |
dc.identifier.citation | Berchiolli, M. et al. (2023) 'Fully Automatic Thoracic Cavity Segmentation in Dynamic Contrast Enhanced Breast MRI Using Deep Convolutional Neural Networks', Applied Sciences (Switzerland), 13 (18), 10160, pp. 1 - 16. doi: 10.3390/app131810160. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/27553 | - |
dc.description | Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical restrictions. | en_US |
dc.description.abstract | Copyright © 2023 by the authors. Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is regarded as one of the main diagnostic tools for breast cancer. Several methodologies have been developed to automatically localize suspected malignant breast lesions. Changes in tissue appearance in response to the injection of the contrast agent (CA) are indicative of the presence of malignant breast lesions. However, these changes are extremely similar to the ones of internal organs, such as the heart. Thus, the task of chest cavity segmentation is necessary for the development of lesion detection. In this work, a data-efficient approach is proposed, to automatically segment breast MRI data. Specifically, a study on several UNet-like architectures (Dynamic UNet) based on ResNet is presented. Experiments quantify the impact of several additions to baseline models of varying depth, such as self-attention and the presence of a bottlenecked connection. The proposed methodology is demonstrated to outperform the current state of the art both in terms of data efficiency and in terms of similarity index when compared to manually segmented data. | en_US |
dc.description.sponsorship | Marco Berchiolli and Susann Wolfram received financial support provided by UK Research and Innovation (project reference: 104192). | en_US |
dc.format.extent | 1 - 16 | - |
dc.format.medium | Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | MDPI | en_US |
dc.rights | Copyright © 2023 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 (https://creativecommons.org/licenses/by/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | DCE-MRI | en_US |
dc.subject | breast segmentation | en_US |
dc.subject | deep learning | en_US |
dc.title | Fully Automatic Thoracic Cavity Segmentation in Dynamic Contrast Enhanced Breast MRI Using Deep Convolutional Neural Networks | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.3390/app131810160 | - |
dc.relation.isPartOf | Applied Sciences (Switzerland) | - |
pubs.issue | 18 | - |
pubs.publication-status | Published | - |
pubs.volume | 13 | - |
dc.identifier.eissn | 2076-3417 | - |
dc.rights.holder | The authors | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers Brunel Innovation Centre |
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FullText.pdf | Copyright © 2023 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 (https://creativecommons.org/licenses/by/4.0/). | 3.53 MB | Adobe PDF | View/Open |
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