Brain tumour segmentation using a triplanar ensemble of U-Nets on MR images
Gliomas appear with wide variation in their characteristics both in terms of their appearance and location on brain MR images, which makes robust tumour segmentation highly challenging, and leads to high inter-rater variability even in manual segmentations. In this work, we propose a triplanar ensem...
| Main Authors: | , , |
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| Format: | Book section |
| Language: | English |
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Springer
2021
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| _version_ | 1797054010760364032 |
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| author | Sundaresan, V Griffanti, L Jenkinson, M |
| author_facet | Sundaresan, V Griffanti, L Jenkinson, M |
| author_sort | Sundaresan, V |
| collection | OXFORD |
| description | Gliomas appear with wide variation in their characteristics both in terms of their appearance and location on brain MR images, which makes robust tumour segmentation highly challenging, and leads to high inter-rater variability even in manual segmentations. In this work, we propose a triplanar ensemble network, with an independent tumour core prediction module, for accurate segmentation of these tumours and their sub-regions. On evaluating our method on the MICCAI Brain Tumor Segmentation (BraTS) challenge validation dataset, for tumour sub-regions, we achieved a Dice similarity coefficient of 0.77 for both enhancing tumour (ET) and tumour core (TC). In the case of the whole tumour (WT) region, we achieved a Dice value of 0.89, which is on par with the top-ranking methods from BraTS’17-19. Our method achieved an evaluation score that was the equal 5th highest value (with our method ranking in 10th place) in the BraTS’20 challenge, with mean Dice values of 0.81, 0.89 and 0.84 on ET, WT and TC regions respectively on the BraTS’20 unseen test dataset. |
| first_indexed | 2024-03-06T18:51:33Z |
| format | Book section |
| id | oxford-uuid:10694183-af1a-4010-ac19-4fc7d9691427 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T18:51:33Z |
| publishDate | 2021 |
| publisher | Springer |
| record_format | dspace |
| spelling | oxford-uuid:10694183-af1a-4010-ac19-4fc7d96914272022-03-26T09:56:13ZBrain tumour segmentation using a triplanar ensemble of U-Nets on MR imagesBook sectionhttp://purl.org/coar/resource_type/c_1843uuid:10694183-af1a-4010-ac19-4fc7d9691427EnglishSymplectic ElementsSpringer2021Sundaresan, VGriffanti, LJenkinson, MGliomas appear with wide variation in their characteristics both in terms of their appearance and location on brain MR images, which makes robust tumour segmentation highly challenging, and leads to high inter-rater variability even in manual segmentations. In this work, we propose a triplanar ensemble network, with an independent tumour core prediction module, for accurate segmentation of these tumours and their sub-regions. On evaluating our method on the MICCAI Brain Tumor Segmentation (BraTS) challenge validation dataset, for tumour sub-regions, we achieved a Dice similarity coefficient of 0.77 for both enhancing tumour (ET) and tumour core (TC). In the case of the whole tumour (WT) region, we achieved a Dice value of 0.89, which is on par with the top-ranking methods from BraTS’17-19. Our method achieved an evaluation score that was the equal 5th highest value (with our method ranking in 10th place) in the BraTS’20 challenge, with mean Dice values of 0.81, 0.89 and 0.84 on ET, WT and TC regions respectively on the BraTS’20 unseen test dataset. |
| spellingShingle | Sundaresan, V Griffanti, L Jenkinson, M Brain tumour segmentation using a triplanar ensemble of U-Nets on MR images |
| title | Brain tumour segmentation using a triplanar ensemble of U-Nets on MR images |
| title_full | Brain tumour segmentation using a triplanar ensemble of U-Nets on MR images |
| title_fullStr | Brain tumour segmentation using a triplanar ensemble of U-Nets on MR images |
| title_full_unstemmed | Brain tumour segmentation using a triplanar ensemble of U-Nets on MR images |
| title_short | Brain tumour segmentation using a triplanar ensemble of U-Nets on MR images |
| title_sort | brain tumour segmentation using a triplanar ensemble of u nets on mr images |
| work_keys_str_mv | AT sundaresanv braintumoursegmentationusingatriplanarensembleofunetsonmrimages AT griffantil braintumoursegmentationusingatriplanarensembleofunetsonmrimages AT jenkinsonm braintumoursegmentationusingatriplanarensembleofunetsonmrimages |