TY - GEN
T1 - Hippocampus segmentation from MR infant brain images via boundary regression
AU - Shao, Yeqin
AU - Guo, Yanrong
AU - Gao, Yaozong
AU - Yang, Xin
AU - Shen, Dinggang
PY - 2016
Y1 - 2016
N2 - Hippocampus segmentation from MR infant brain images is indispensable for studying early brain development. However, most of hippocampus segmentation methods were developed for adult brain images, which are not suitable for infant brain images of the first year due to low image contrast and variable structural patterns of early hippocampal development. To address these challenges, we propose a boundary regression method to detect hippocampal boundaries in the infant brain images, and then use the obtained boundaries to guide the deformable segmentation. The advantages of our segmentation method are: (1) different from the recently-developed atlas-based hippocampus segmentation methods, our method does not perform time-consuming deformable registrations; (2) different from the conventional point-regression-based boundary detection methods, our boundary regression method can predict the whole hippocampal boundary by a single regression model. Experiments on MR infant brain images from 2-week-old to 1-year-old show promising hippocampus segmentation results.
AB - Hippocampus segmentation from MR infant brain images is indispensable for studying early brain development. However, most of hippocampus segmentation methods were developed for adult brain images, which are not suitable for infant brain images of the first year due to low image contrast and variable structural patterns of early hippocampal development. To address these challenges, we propose a boundary regression method to detect hippocampal boundaries in the infant brain images, and then use the obtained boundaries to guide the deformable segmentation. The advantages of our segmentation method are: (1) different from the recently-developed atlas-based hippocampus segmentation methods, our method does not perform time-consuming deformable registrations; (2) different from the conventional point-regression-based boundary detection methods, our boundary regression method can predict the whole hippocampal boundary by a single regression model. Experiments on MR infant brain images from 2-week-old to 1-year-old show promising hippocampus segmentation results.
UR - http://www.scopus.com/inward/record.url?scp=84981316114&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84981316114&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-42016-5_14
DO - 10.1007/978-3-319-42016-5_14
M3 - Conference contribution
AN - SCOPUS:84981316114
SN - 9783319420158
VL - 9601
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 146
EP - 154
BT - Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers
PB - Springer Verlag
T2 - International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI
Y2 - 9 October 2015 through 9 October 2015
ER -