@inproceedings{846be5ec88b84bc0a511173ae867d81f,
title = "3D deeply supervised network for automatic liver segmentation from CT volumes",
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.",
author = "Qi Dou and Hao Chen and Yueming Jin and Lequan Yu and Jing Qin and Heng, {Pheng Ann}",
year = "2016",
month = jan,
day = "1",
doi = "10.1007/978-3-319-46723-8_18",
language = "English",
isbn = "9783319467221",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "149--157",
booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings",
address = "Germany",
}