TY - GEN
T1 - Fully automated esophagus segmentation with a hierarchical deep learning approach
AU - Trullo, Roger
AU - Petitjean, Caroline
AU - Nie, Dong
AU - Shen, Dinggang
AU - Ruan, Su
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017
Y1 - 2017
N2 - Segmentation of organs at risk in CT volumes is a prerequisite for radiotherapy treatment planning. In this paper we focus on esophagus segmentation, a challenging problem since the walls of the esophagus have a very low contrast in CT images. Making use of Fully Convolutional Networks (FCN), we present several extensions that improve the performance, including a new architecture that allows to use low level features with high level information, effectively combining local and global information for improving the localization accuracy. Experiments demonstrate competitive performance on a dataset of 30 CT scans.
AB - Segmentation of organs at risk in CT volumes is a prerequisite for radiotherapy treatment planning. In this paper we focus on esophagus segmentation, a challenging problem since the walls of the esophagus have a very low contrast in CT images. Making use of Fully Convolutional Networks (FCN), we present several extensions that improve the performance, including a new architecture that allows to use low level features with high level information, effectively combining local and global information for improving the localization accuracy. Experiments demonstrate competitive performance on a dataset of 30 CT scans.
UR - http://www.scopus.com/inward/record.url?scp=85041387177&partnerID=8YFLogxK
U2 - 10.1109/ICSIPA.2017.8120664
DO - 10.1109/ICSIPA.2017.8120664
M3 - Conference contribution
AN - SCOPUS:85041387177
T3 - Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017
SP - 503
EP - 506
BT - Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017
Y2 - 12 September 2017 through 14 September 2017
ER -