Young, Fiona;
Aquilina, Kristian;
Clayden, Jonathan D;
Clark, Chris A;
(2023)
Training data requirements for atlas-based brain fibre tract identification.
In:
Proceedings of the IEEE EMBS International Conference on Data Science and Engineering in Healthcare, Medicine & Biology.
(pp. pp. 1-2).
Institute of Electrical and Electronics Engineers (IEEE)
(In press).
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Abstract
Large volumes of annotated training data are often required for data-driven image analysis methods. We consider two techniques for identifying brain fibre bundles from diffusion MRI scans, tractfinder and TractSeg, and compare performances using different amounts of training data. Our results show that tractfinder, an atlas-based method, shows no improvement in performance beyond a relatively small number of training samples. This is an advantage in a field where generating and maintaining high quality reference data is difficult and time-consuming.
| Type: | Proceedings paper |
|---|---|
| Title: | Training data requirements for atlas-based brain fibre tract identification |
| Event: | 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology |
| Location: | St. Julian's, Malta |
| Dates: | 7th-9th December 2023 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://datascience.embs.org/2023/ |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | medical imaging, deel learning, diffusion MRI, segmentation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10181046 |
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