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Title: | Fully Automatic Thoracic Cavity Segmentation in Dynamic Contrast Enhanced Breast MRI Using Deep Convolutional Neural Networks |
Authors: | Berchiolli, M Wolfram, S Balachandran, W Gan, T-H |
Keywords: | DCE-MRI;breast segmentation;deep learning |
Issue Date: | 9-Sep-2023 |
Publisher: | MDPI |
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. |
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. |
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. |
URI: | https://bura.brunel.ac.uk/handle/2438/27553 |
DOI: | https://doi.org/10.3390/app131810160 |
Other Identifiers: | ORCID iD: Wamadeva Balachandran https://orcid.org/0000-0002-4806-2257 ORCID iD: Tat-Hean Gan https://orcid.org/0000-0002-5598-8453 10160 |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers Brunel Innovation Centre |
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