TY - GEN
T1 - LATEST
T2 - International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
AU - Wang, Li
AU - Gao, Yaozong
AU - Li, Gang
AU - Shi, Feng
AU - Lin, Weili
AU - Shen, Dinggang
N1 - Funding Information:
This work was supported in part by National Institutes of Health grants (MH100217, MH070890, EB006733, EB008374, EB009634, AG041721, AG042599, MH088520, MH108914, MH107815, and MH109773).
Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Accurate segmentation of isointense infant (~6 months of age) brain MRIs is of great importance, however, a very challenging task, due to extremely low tissue contrast caused by ongoing myelination processes. In this work, we propose a novel learning method based on Local AdapTivE and Sequential Training (LATEST) for segmentation. Specifically, random forest technique is employed to train a local classifier (a single decision tree) for each voxel in the common space based on the neighboring training samples from atlases. Then, for each given voxel, all trained nearby individual classifiers (decision trees) are grouped together to form a forest. Moreover, the estimated probabilities are further used as additional source images to train the next set of local classifiers for refining tissue classification. By iteratively training the subsequent classifiers based on the updated tissue probability maps, a sequence of local classifiers can be built for accurate tissue segmentation.
AB - Accurate segmentation of isointense infant (~6 months of age) brain MRIs is of great importance, however, a very challenging task, due to extremely low tissue contrast caused by ongoing myelination processes. In this work, we propose a novel learning method based on Local AdapTivE and Sequential Training (LATEST) for segmentation. Specifically, random forest technique is employed to train a local classifier (a single decision tree) for each voxel in the common space based on the neighboring training samples from atlases. Then, for each given voxel, all trained nearby individual classifiers (decision trees) are grouped together to form a forest. Moreover, the estimated probabilities are further used as additional source images to train the next set of local classifiers for refining tissue classification. By iteratively training the subsequent classifiers based on the updated tissue probability maps, a sequence of local classifiers can be built for accurate tissue segmentation.
UR - http://www.scopus.com/inward/record.url?scp=85025134939&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-61188-4_3
DO - 10.1007/978-3-319-61188-4_3
M3 - Conference contribution
AN - SCOPUS:85025134939
SN - 9783319611877
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 26
EP - 34
BT - Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers
A2 - Arbel, Tal
A2 - Langs, Georg
A2 - Jenkinson, Mark
A2 - Menze, Bjoern
A2 - Wells III, William M.
A2 - Chung, Albert C.S.
A2 - Kelm, B. Michael
A2 - Cai, Weidong
A2 - Montillo, Albert
A2 - Metaxas, Dimitris
A2 - Cardoso, M. Jorge
A2 - Zhang, Shaoting
A2 - Ribbens, Annemie
A2 - Muller, Henning
PB - Springer Verlag
Y2 - 21 October 2016 through 21 October 2016
ER -