TY - GEN
T1 - Learning-based topological correction for infant cortical surfaces
AU - Hao, Shijie
AU - Li, Gang
AU - Wang, Li
AU - Meng, Yu
AU - Shen, Dinggang
N1 - Funding Information:
This work was supported in part by NIH grants (MH107815, MH108914, MH100217, EB006733, EB008374, and EB009634). Dr. Shijie Hao was supported by National Nature Science Foundation of China grant 61301222.
Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - Reconstruction of topologically correct and accurate cortical surfaces from infant MR images is of great importance in neuroimaging mapping of early brain development. However,due to rapid growth and ongoing myelination,infant MR images exhibit extremely low tissue contrast and dynamic appearance patterns,thus leading to much more topological errors (holes and handles) in the cortical surfaces derived from tissue segmentation results,in comparison to adult MR images which typically have good tissue contrast. Existing methods for topological correction either rely on the minimal correction criteria,or ad hoc rules based on image intensity priori,thus often resulting in erroneous correction and large anatomical errors in reconstructed infant cortical surfaces. To address these issues,we propose to correct topological errors by learning information from the anatomical references,i.e.,manually corrected images. Specifically,in our method,we first locate candidate voxels of topologically defected regions by using a topology-preserving level set method. Then,by leveraging rich information of the corresponding patches from reference images,we build regionspecific dictionaries from the anatomical references and infer the correct labels of candidate voxels using sparse representation. Notably,we further integrate these two steps into an iterative framework to enable gradual correction of large topological errors,which are frequently occurred in infant images and cannot be completely corrected using one-shot sparse representation. Extensive experiments on infant cortical surfaces demonstrate that our method not only effectively corrects the topological defects,but also leads to better anatomical consistency,compared to the state-of-the-art methods.
AB - Reconstruction of topologically correct and accurate cortical surfaces from infant MR images is of great importance in neuroimaging mapping of early brain development. However,due to rapid growth and ongoing myelination,infant MR images exhibit extremely low tissue contrast and dynamic appearance patterns,thus leading to much more topological errors (holes and handles) in the cortical surfaces derived from tissue segmentation results,in comparison to adult MR images which typically have good tissue contrast. Existing methods for topological correction either rely on the minimal correction criteria,or ad hoc rules based on image intensity priori,thus often resulting in erroneous correction and large anatomical errors in reconstructed infant cortical surfaces. To address these issues,we propose to correct topological errors by learning information from the anatomical references,i.e.,manually corrected images. Specifically,in our method,we first locate candidate voxels of topologically defected regions by using a topology-preserving level set method. Then,by leveraging rich information of the corresponding patches from reference images,we build regionspecific dictionaries from the anatomical references and infer the correct labels of candidate voxels using sparse representation. Notably,we further integrate these two steps into an iterative framework to enable gradual correction of large topological errors,which are frequently occurred in infant images and cannot be completely corrected using one-shot sparse representation. Extensive experiments on infant cortical surfaces demonstrate that our method not only effectively corrects the topological defects,but also leads to better anatomical consistency,compared to the state-of-the-art methods.
UR - http://www.scopus.com/inward/record.url?scp=84996587401&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46720-7_26
DO - 10.1007/978-3-319-46720-7_26
M3 - Conference contribution
AN - SCOPUS:84996587401
SN - 9783319467191
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 219
EP - 227
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
A2 - Ourselin, Sebastian
A2 - Joskowicz, Leo
A2 - Sabuncu, Mert R.
A2 - Wells, William
A2 - Unal, Gozde
PB - Springer Verlag
T2 - 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
Y2 - 21 October 2016 through 21 October 2016
ER -