Generalizability of deep learning models on brain tumour segmentation

Duman, Abdulkerim, Powell, J, Thomas, S, Sun, Xianfang ORCID: https://orcid.org/0000-0002-6114-0766 and Spezi, Emiliano ORCID: https://orcid.org/0000-0002-1452-8813 2024. Generalizability of deep learning models on brain tumour segmentation. Presented at: Cardiff University Engineering Research Conference 2023, Cardiff, UK, 12-14 July 2023. Published in: Spezi, Emiliano and Bray, Michaela eds. Proceedings of the Cardiff University Engineering Research Conference 2023. Cardiff: Cardiff University Press, pp. 3-5. 10.18573/conf1.b

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Abstract

Brain tumour segmentation is a hard and time-consuming task to be conducted in the process of radiotherapy planning. Deep Learning (DL) applications have a significant improvement in image segmentation tasks. In this work, we apply DL models such as 2D and 2.5D U-NET to the segmentation task of a brain tumour on the BraTS 2021 dataset and our local dataset. The 2.5D network is a modified version of 2D U-NET by using three slices as an input for each magnetic resonance imaging (MRI) sequence. We achieve the best segmentation results with 2.5D U-NET on BraTS with Dice scores of 86.97%, 91.27% and 94.42% for enhancing tumour, tumour core and whole tumour respectively. On the other hand, our best segmentation result of the GTV delineation on the local dataset is a Dice score of 78.51% for 2D U-NET. Although the result of GTV contours is not improved by 2.5D for the local dataset due to non-fixed voxel size, the Dice scores of ET, TC and WT are improved by the proposed 2.5D U-NET for the BraTS dataset.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics Engineering
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) R Medicine > RD Surgery T Technology > TA Engineering (General). Civil engineering (General)
Additional Information:	Contents are extended abstracts of papers, not full papers
Publisher:	Cardiff University Press
ISBN:	978-1-9116-5349-3
Date of First Compliant Deposit:	10 June 2024
Last Modified:	26 Jul 2024 13:32
URI:	https://orca.cardiff.ac.uk/id/eprint/169666

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