dc.contributor.author |
Shen, Jiantao |
|
dc.contributor.author |
Sharifzadeh-Kermani, Alireza |
|
dc.contributor.author |
Tayebi, Maryam |
|
dc.contributor.author |
Kwon, Eryn |
|
dc.contributor.author |
Guild, Sarah-Jane |
|
dc.contributor.author |
Abbasi, Hamid |
|
dc.contributor.author |
Holdsworth, Samantha |
|
dc.contributor.author |
Talou, Gonzalo Maso |
|
dc.contributor.author |
Safaei, Soroush |
|
dc.coverage.spatial |
United States |
|
dc.date.accessioned |
2024-03-13T21:36:03Z |
|
dc.date.available |
2024-03-13T21:36:03Z |
|
dc.date.issued |
2023-07 |
|
dc.identifier.citation |
(2023). 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2023, 1-4. |
|
dc.identifier.issn |
2375-7477 |
|
dc.identifier.uri |
https://hdl.handle.net/2292/67696 |
|
dc.description.abstract |
Automated 3D brain segmentation methods have been shown to produce fast, reliable, and reproducible segmentations from magnetic resonance imaging (MRI) sequences for the anatomical structures of the human brain. Despite the extensive experimental research utility of large animal species such as the sheep, there is limited literature on the segmentation of their brains relative to that of humans. The availability of automatic segmentation algorithms for animal brain models can have significant impact for experimental explorations, such as treatment planning and studying brain injuries. The neuroanatomical similarities in size and structure between sheep and humans, plus their long lifespan and docility, make them an ideal animal model for investigating automatic segmentation methods.This work, for the first time, proposes an atlas-free fully automatic sheep brain segmentation tool that only requires structural MR images (T1-MPRAGE images) to segment the entire sheep brain in less than one minute. We trained a convolutional neural network (CNN) model - namely a four-layer U-Net - on data from eleven adult sheep brains (training and validation: 8 sheep, testing: 3 sheep), with a high overall Dice overlap score of 93.7%.Clinical relevance- Upon future validation on larger datasets, our atlas-free automatic segmentation tool can have clinical utility and contribute towards developing robust and fully automatic segmentation tools which could compete with atlas-based tools currently available. |
|
dc.format.medium |
Print |
|
dc.language |
eng |
|
dc.publisher |
Institute of Electrical and Electronics Engineers (IEEE) |
|
dc.relation.ispartofseries |
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference |
|
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
|
dc.subject |
Brain |
|
dc.subject |
Animals |
|
dc.subject |
Sheep |
|
dc.subject |
Humans |
|
dc.subject |
Magnetic Resonance Imaging |
|
dc.subject |
Algorithms |
|
dc.subject |
Image Processing, Computer-Assisted |
|
dc.subject |
Adult |
|
dc.subject |
Neural Networks, Computer |
|
dc.subject |
5105 Medical and Biological Physics |
|
dc.subject |
51 Physical Sciences |
|
dc.subject |
Bioengineering |
|
dc.subject |
Neurosciences |
|
dc.subject |
Biomedical Imaging |
|
dc.subject |
Brain Disorders |
|
dc.subject |
Networking and Information Technology R&D (NITRD) |
|
dc.subject |
Neurological |
|
dc.title |
Atlas-Free Automatic Segmentation of Sheep Brain MRI |
|
dc.type |
Conference |
|
dc.identifier.doi |
10.1109/embc40787.2023.10340739 |
|
pubs.begin-page |
1 |
|
pubs.volume |
2023 |
|
dc.date.updated |
2024-02-16T09:57:16Z |
|
dc.rights.holder |
Copyright: IEEE |
en |
dc.identifier.pmid |
38083135 (pubmed) |
|
pubs.author-url |
https://ieeexplore.ieee.org/document/10340739 |
|
pubs.end-page |
4 |
|
pubs.publication-status |
Published |
|
dc.rights.accessrights |
http://purl.org/eprint/accessRights/RetrictedAccess |
en |
pubs.subtype |
Proceedings |
|
pubs.elements-id |
1003502 |
|
pubs.org-id |
Bioengineering Institute |
|
pubs.org-id |
Medical and Health Sciences |
|
pubs.org-id |
Medical Sciences |
|
pubs.org-id |
Anatomy and Medical Imaging |
|
pubs.org-id |
Physiology Division |
|
dc.identifier.eissn |
2694-0604 |
|
pubs.record-created-at-source-date |
2024-02-16 |
|