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Communication Dans Un Congrès Année : 2022

Diffusion Tensor Imaging Biomarkers for Parkinson’s Disease Symptomatology

Résumé

Voxel-based analysis is an invaluable tool for biomarker discovery in population neuroimaging. The traditional approach however is limited to local, linear biomarkers, determining if the linear correlation between the quantitative value of an image is correlated with the disease state at a single voxel. By analysing convolutional neural networks that directly predict clinical scores using a newly proposed voxel-based diktiometry, non-linear and non-local biomarkers can be visualised, leading to an additional tool for biomarker discovery. Our approach using diffusion tensor images to predict UPDRS3 and Hoehn and Yahr scores for Parkinson’s disease patients, shows consistent and explainable results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Dates et versions

hal-03931176 , version 1 (09-01-2023)

Identifiants

Citer

A. Estudillo Romero, C Haegelen, P. Jannin, John S H Baxter. Diffusion Tensor Imaging Biomarkers for Parkinson’s Disease Symptomatology. 1st Workshop on Artificial Intelligence over Infrared Images for Medical Applications, AIIIMA 2022, and the 1st Workshop on Medical Image Assisted Biomarker Discovery, MIABID 2022, both held in conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022, Sep 2022, Singapore, Singapore. pp.134-142, ⟨10.1007/978-3-031-19660-7_13⟩. ⟨hal-03931176⟩
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