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

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其它資訊
摘要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.
著者Qi Dou, Hao Chen, Yueming Jin. Lequan Yu, Jing Qin, Pheng-Ann Heng
會議名稱19th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
會議開始日17.10.2016
會議完結日21.10.2016
會議地點Athens
會議國家/地區希臘
會議論文集題名Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016
系列標題Lecture Notes in Computer Science
出版年份2016
月份10
日期17
卷號9901
出版社Springer International Publishing
頁次149 - 157
國際標準書號978-3-319-46722-1
電子國際標準書號978-3-319-46723-8
國際標準期刊號0302-9743
電子國際標準期刊號1611-3349
語言美式英語
關鍵詞Automatic liver segmentation, Deeply supervised network, Deep learning, Medical image segmentaition

上次更新時間 2021-12-05 於 02:42