Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/67169
Title: 3D deeply supervised network for automatic liver segmentation from CT volumes
Authors: Dou, Q
Chen, H
Jin, Y
Yu, L
Qin, J 
Heng, PA
Issue Date: 2016
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2016, v. 9901, p. 149-157 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Automatic liver segmentation from CT volumes is a crucial prerequisite yet challenging task for computer-aided hepatic disease diagnosis and treatment. In this paper,we present a novel 3D deeply supervised network (3D DSN) to address this challenging task. The proposed 3D DSN takes advantage of a fully convolutional architecture which performs efficient end-to-end learning and inference. More importantly,we introduce a deep supervision mechanism during the learning process to combat potential optimization difficulties,and thus the model can acquire a much faster convergence rate and more powerful discrimination capability. On top of the high-quality score map produced by the 3D DSN,a conditional random field model is further employed to obtain refined segmentation results. We evaluated our framework on the public MICCAI-SLiver07 dataset. Extensive experiments demonstrated that our method achieves competitive segmentation results to state-of-the-art approaches with a much faster processing speed.
Description: 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Greece, 17-21 October, 2016
URI: http://hdl.handle.net/10397/67169
ISBN: 9783319467221
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-319-46723-8_18
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