PT  - JOURNAL ARTICLE
AU  - Oscar Esteban
AU  - Dominique Zosso
AU  - Alessandro Daducci
AU  - Meritxell Bach-Cuadra
AU  - María-J. Ledesma-Carbayo
AU  - Jean-Philippe Thiran
AU  - Andres Santos
TI  - Active contours-driven registration method for the structure-informed segmentation of diffusion MR images
AID  - 10.1101/018945
DP  - 2015 Jan 01
TA  - bioRxiv
PG  - 018945
4099  - http://biorxiv.org/content/early/2015/05/05/018945.short
4100  - http://biorxiv.org/content/early/2015/05/05/018945.full
AB  - Current applications of whole-brain tractography to diffusion MRI data require highly precise delineations of anatomical structures, which are usually projected from T1 weighted images by registration. In this study, we propose regseg, which is a simultaneous segmentation and registration method that uses active contours without edges extracted from structural images. The contours evolve through a free-form deformation field supported by the B-spline basis to optimally map the contours onto the data in the target space. We tested the functionality of regseg using four digital phantoms warped with known and randomly generated deformations, where subvoxel accuracy was achieved. We then applied regseg to a registration/segmentation task using 16 real diffusion MRI datasets from the Human Connectome Project, which were warped by realistic and nonlinear distortions that are typically present in these data. We computed the misregistration error of the contours estimated by regseg with respect to their theoretical location using the ground truth, thereby obtaining a 95% CI of 0.56–0.66 mm distance between corresponding mesh vertices, which was below the 1.25 mm isotropic resolution of the images. We also compared the performance of our proposed method with a widely used registration tool, which showed that regseg outperformed this method in our settings.