| Full text | |
| Author(s): |
Pinheiro, Gustavo Retuci
;
Brusini, Lorenza
;
Carmo, Diedre
;
Proa, Renata
;
Abreu, Thays
;
Appenzeller, Simone
;
Menegaz, Gloria
;
Rittner, Leticia
Total Authors: 8
|
| Document type: | Journal article |
| Source: | APPLIED SCIENCES-BASEL; v. 13, n. 9, p. 21-pg., 2023-04-23. |
| Abstract | |
The thalamus is a subcortical brain structure linked to the motor system. Since certain changes within this structure are related to diseases, such as multiple sclerosis and Parkinson's, the characterization of the thalamus-e.g., shape assessment-is a crucial step in relevant studies and applications, including medical research and surgical planning. A robust and reliable thalamus-segmentation method is therefore, required to meet these demands. Despite presenting low contrast for this particular structure, T1-weighted imaging is still the most common MRI sequence for thalamus segmentation. However, diffusion MRI (dMRI) captures different micro-structural details of the biological tissue and reveals more contrast of the thalamic borders, thereby serving as a better candidate for thalamus-segmentation methods. Accordingly, we propose a baseline multimodality thalamus-segmentation pipeline that combines dMRI and T1-weighted images within a CNN approach, achieving state-of-the-art levels of Dice overlap. Furthermore, we are hosting an open benchmark with a large, preprocessed, publicly available dataset that includes co-registered, T1-weighted, dMRI, manual thalamic masks; masks generated by three distinct automated methods; and a STAPLE consensus of the masks. The dataset, code, environment, and instructions for the benchmark leaderboard can be found on our GitHub and CodaLab. (AU) | |
| FAPESP's process: | 19/21964-4 - Lung diseases and COVID diagnosis from computed tomography images using explainable deep learning |
| Grantee: | Diedre Santos do Carmo |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 13/07559-3 - BRAINN - The Brazilian Institute of Neuroscience and Neurotechnology |
| Grantee: | Fernando Cendes |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |