Machine Learning Framework For Fully Automatic Quality Checking Of Rigid And Affine Registrations In Big Data Brain MRI
Tummala, S.
Show abstract
Rigid and affine registrations to a common template are the essential steps during pre-processing of brain structural magnetic resonance imaging (MRI) data. Manual quality check (QC) of these registrations is quite tedious if the data contains several thousands of images. Therefore, we propose a machine learning (ML) framework for fully automatic QC of these registrations via local computation of the similarity functions such as normalized cross-correlation, normalized mutual-information, and correlation ratio, and using these as features for training of different ML classifiers. To facilitate supervised learning, misaligned images are generated. A structural MRI dataset consisting of 215 subjects from autism brain imaging data exchange is used for 5-fold cross-validation and testing. Few classifiers such as kNN, AdaBoost, and random forest reached testing F1-scores of 0.98 for QC of both rigid and affine registrations. These tested ML models could be deployed for practical use.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- A preliminary attempt to harmonize using physics-constrained deep neural networks for multisite and multiscanner MRI datasets (PhyCHarm) 97%
- Cerebral artery segmentation based on magnetization-prepared two rapid acquisition gradient echo multi-contrast images in 7 Tesla magnetic resonance imaging 95%
- Improved motion correction of submillimetre 7T fMRI time series with boundary-based registration (BBR) 95%
Similar papers in this journal
Similar papers in this journal
- Anisotropy Measure from Three Diffusion-Encoding Gradient Directions 96%
- Estimation of in-scanner head pose changes during structural MRI using a convolutional neural network trained on eye tracker video 96%
- MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal 95%
Similar papers in this journal
- Freewater EstimatoR using iNtErpolated iniTialization (FERNET): Toward Accurate Estimation of Free Water in Peritumoral Region Using Single-Shell Diffusion MRI Data 96%
- Regional brain development analysis through registration using anisotropic similarity, a constrained affine transformation 94%
- Optimizing the intrinsic parallel diffusivity in NODDI: an extensive empirical evaluation 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.