21 March 2016 Segmentation of thalamus from MR images via task-driven dictionary learning
Author Affiliations +
Abstract
Automatic thalamus segmentation is useful to track changes in thalamic volume over time. In this work, we introduce a task-driven dictionary learning framework to find the optimal dictionary given a set of eleven features obtained from T1-weighted MRI and diffusion tensor imaging. In this dictionary learning framework, a linear classifier is designed concurrently to classify voxels as belonging to the thalamus or non-thalamus class. Morphological post-processing is applied to produce the final thalamus segmentation. Due to the uneven size of the training data samples for the non-thalamus and thalamus classes, a non-uniform sampling scheme is pro- posed to train the classifier to better discriminate between the two classes around the boundary of the thalamus. Experiments are conducted on data collected from 22 subjects with manually delineated ground truth. The experimental results are promising in terms of improvements in the Dice coefficient of the thalamus segmentation overstate-of-the-art atlas-based thalamus segmentation algorithms.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Luoluo Liu, Luoluo Liu, Jeffrey Glaister, Jeffrey Glaister, Xiaoxia Sun, Xiaoxia Sun, Aaron Carass, Aaron Carass, Trac D. Tran, Trac D. Tran, Jerry L. Prince, Jerry L. Prince, "Segmentation of thalamus from MR images via task-driven dictionary learning", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97843H (21 March 2016); doi: 10.1117/12.2214206; https://doi.org/10.1117/12.2214206
PROCEEDINGS
7 PAGES


SHARE
Back to Top