TY - GEN
T1 - Interactive registration and segmentation for multi-atlas-based labeling of brain MR image
AU - Wang, Qian
AU - Wu, Guorong
AU - Kim, Min Jeong
AU - Zhang, Lichi
AU - Shen, Dinggang
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2015.
PY - 2015
Y1 - 2015
N2 - In the conventional multi-atlas-based labeling methods, atlases are registered with each unlabeled image, which is then segmented by fusing the labels of all registered atlases. The registration is typically ignorant about the segmentation while the segmentation of each individual unlabeled image is independently considered, both of which potentially undermine the accuracy in labeling. In this work, we propose the interactive registration-segmentation scheme for multi-atlas- based labeling of brain MR images. First, we learn the distribution of all images (including atlases and unlabeled images) and register them to their common space in the groupwise manner. Then, we segment all unlabeled images simultaneously, by fusing the labels of the registered atlases in the common space as well as the tentative segmentation of the unlabeled images. Next, the (tentative) labeling feeds back to refine the registration, thus all images are more accurately aligned within the common space. The improved registration further boosts the accuracy to determine the segmentation of the unlabeled images. According to our experimental results, the iterative optimization to the interactive registration-segmentation scheme can improve the performances of the multi-atlas-based labeling significantly.
AB - In the conventional multi-atlas-based labeling methods, atlases are registered with each unlabeled image, which is then segmented by fusing the labels of all registered atlases. The registration is typically ignorant about the segmentation while the segmentation of each individual unlabeled image is independently considered, both of which potentially undermine the accuracy in labeling. In this work, we propose the interactive registration-segmentation scheme for multi-atlas- based labeling of brain MR images. First, we learn the distribution of all images (including atlases and unlabeled images) and register them to their common space in the groupwise manner. Then, we segment all unlabeled images simultaneously, by fusing the labels of the registered atlases in the common space as well as the tentative segmentation of the unlabeled images. Next, the (tentative) labeling feeds back to refine the registration, thus all images are more accurately aligned within the common space. The improved registration further boosts the accuracy to determine the segmentation of the unlabeled images. According to our experimental results, the iterative optimization to the interactive registration-segmentation scheme can improve the performances of the multi-atlas-based labeling significantly.
UR - http://www.scopus.com/inward/record.url?scp=84951282632&partnerID=8YFLogxK
U2 - 10.1007/978-3-662-48558-3_24
DO - 10.1007/978-3-662-48558-3_24
M3 - Conference contribution
AN - SCOPUS:84951282632
SN - 9783662485576
T3 - Communications in Computer and Information Science
SP - 240
EP - 248
BT - Computer Vision CCF Chinese Conference, CCCV 2015, Proceedings
A2 - Chen, Xilin
A2 - Zha, Hongbin
A2 - Miao, Qiguang
A2 - Wang, Liang
PB - Springer Verlag
T2 - 1st Chinese Conference on Computer Vision, CCCV 2015
Y2 - 18 September 2015 through 20 September 2015
ER -