3D deeply supervised network for automatic liver segmentation from CT volumes

Qi Dou, Hao Chen, Yueming Jin, Lequan Yu, Jing Qin, Pheng Ann Heng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

140 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
PublisherSpringer Verlag
Pages149-157
Number of pages9
ISBN (Print)9783319467221
DOIs
Publication statusPublished - 1 Jan 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9901 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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