3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes
Refereed conference paper presented and published in conference proceedings

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AbstractAutomatic 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.
All Author(s) ListQi Dou, Hao Chen, Yueming Jin. Lequan Yu, Jing Qin, Pheng-Ann Heng
Name of Conference19th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
Start Date of Conference17/10/2016
End Date of Conference21/10/2016
Place of ConferenceAthens
Country/Region of ConferenceGreece
Proceedings TitleMedical Image Computing and Computer-Assisted Intervention – MICCAI 2016
Series TitleLecture Notes in Computer Science
Volume Number9901
PublisherSpringer International Publishing
Pages149 - 157
LanguagesEnglish-United States
KeywordsAutomatic liver segmentation, Deeply supervised network, Deep learning, Medical image segmentaition

Last updated on 2021-29-11 at 00:01