Brain-SHIFT an innovative AI tool to select Subdural Hematoma Invasive or Follow-up Treatment
Organization:
Funded by: | Pioneers in Healthcare Innovation Fund 2022 |
Postdoc: |  |
Supervisors: | chair MIA:  Daily supervisor:  |
Collaboration: |
Description:
The midline shift is a well-known measurement used in the clinical routine to estimate the deformation of the pathological brain, particularly in the case of a chronic subdural hematoma. However, this measurement is local and does not reflect the pressure encountered by all neurological regions. Then there is an unmet need for new measurements considering the deformation of all parts of the brain.
The aim of this project is to develop a method to estimate the deformation field encountered by the whole brain when a hematoma is growing in the skull, which is a constrained space. First, anatomical landmarks such as the skull, the midplane of the brain and the hematoma volume will be automatically computed. Then, they will be used by a deep learning network learning to estimate a clinically relevant brain deformation field: a deformation field that allows to estimate a pseudo-healthy scan from the original one.
Ultimately this study will allow to develop new measurements that could be used by the clinical team to decide which treatment should be given to patients with chronic subdural hematomas.
Output:
2023
Thibeau-Sutre, E. , Wolterink, J. M., Colliot, O., & Burgos, N. (2023). How Can Data Augmentation Improve Attribution Maps for Disease Subtype Explainability? In O. Colliot, & I. Isgum (Eds.), SPIE Medical Imaging 2023: Image Processing Article 1246424 SPIE. https://doi.org/10.1117/12.2653809
Thibeau-Sutre, E. , Alblas, D., Buurman, S. , Brune, C. , & Wolterink, J. M. (Accepted/In press). Uncertainty-based quality assurance of carotid artery segmentation. In Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE)
2022
Thibeau-Sutre, E., Díaz, M., Hassanaly, R., Routier, A., Dormont, D., Colliot, O., & Burgos, N. (2022). ClinicaDL: An open-source deep learning software for reproducible neuroimaging processing. Computer methods and programs in biomedicine, 220, Article 106818. https://doi.org/10.1016/j.cmpb.2022.106818
Thibeau-Sutre, E., Couvy-Duchesne, B., Dormont, D., Colliot, O., & Burgos, N. (2022). MRI field strength predicts Alzheimer's disease: a case example of bias in the ADNI data set. In MRI field strength predicts Alzheimer's disease: a case example of bias in the ADNI data set https://doi.org/10.1109/ISBI52829.2022.9761504
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