abstract
- Data augmentation techniques that improve the classification and segmentation of natural scenes often do not transfer well to brain imaging data. The conceptually most plausible augmentation technique for biological tissue, elastic deformation, works well on microscopic tissue but is limited on macroscopic structures like the brain, as the majority of mathematically possible elastic deformations of the human brain are anatomically implausible. Here, we characterize the subspace of anatomically plausible deformations for a participant’s brain image by nonlinearly registering the image to the brain images of several reference participants. Using the resulting warp fields for data augmentation outperformed both random elastic deformations and the non-augmented baseline in age prediction from T1 brain images. M.-A. Schulz and A. Koch—Equal contribution. Project was funded by DFG 414984028/CRC-1404) and the Brain & Behavior Research Foundation (NARSAD young investigator grant). Access provided by Humboldt Universität zu Berlin. Download conference paper PDF Similar content being viewed by others Flexible Bayesian Modelling for Nonlinear Image Registration Chapter © 2020 MR Imaging of Human Brain Mechanics In Vivo: New Measurements to Facilitate the Development of Computational Models of Brain Injury Article 01 July 2021 Mapping the Human Brain with Computational Anatomy Chapter © 2024 Explore related subjects Discover the latest articles, books and news in related subjects, suggested using machine learning. Brain Mapping Brain Stimulation Brain-machine Interface Intelligence Augmentation Neuroengineering Neural ageing Data Augmentation Techniques in Deep Learning Applications