Abstract：
PURPOSE:Segmentation of the liver from abdominal computed tomography (CT) images is an essential step in some computer-assisted clinical interventions, such as surgery planning for living donor liver transplant, radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automatically segment the liver in CT scans. METHODS:The proposed method consists of two main steps: (i) simultaneously liver detection and probabilistic segmentation using 3D convolutional neural network; (ii) accuracy refinement of the initial segmentation with graph cut and the previously learned probability map. RESULTS:The proposed approach was validated on forty CT volumes taken from two public databases MICCAI-Sliver07 and 3Dircadb1. For the MICCAI-Sliver07 test dataset, the calculated mean ratios of volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root-mean-square symmetric surface distance (RMSD) and maximum symmetric surface distance (MSD) are 5.9, 2.7 %, 0.91, 1.88 and 18.94 mm, respectively. For the 3Dircadb1 dataset, the calculated mean ratios of VOE, RVD, ASD, RMSD and MSD are 9.36, 0.97 %, 1.89, 4.15 and 33.14 mm, respectively. CONCLUSIONS:The proposed method is fully automatic without any user interaction. Quantitative results reveal that the proposed approach is efficient and accurate for hepatic volume estimation in a clinical setup. The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and nonreproducible manual segmentation method.
journal_name
Int J Comput Assist Radiol Surgauthors
Lu F,Wu F,Hu P,Peng Z,Kong Ddoi
10.1007/s11548-016-1467-3subject
Has Abstractpub_date
2017-02-01 00:00:00pages
171-182issue
2eissn
1861-6410issn
1861-6429pii
10.1007/s11548-016-1467-3journal_volume
12pub_type
杂志文章abstract：PURPOSE:Fusion of pre/perioperative images and intra-operative images may add relevant information during image-guided procedures. In abdominal procedures, respiratory motion changes the position of organs, and thus accurate image guidance requires a continuous update of the spatial alignment of the (pre/perioperative)...
journal_title：International journal of computer assisted radiology and surgery
pub_type： 杂志文章
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journal_title：International journal of computer assisted radiology and surgery
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journal_title：International journal of computer assisted radiology and surgery
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journal_title：International journal of computer assisted radiology and surgery
pub_type： 杂志文章
doi：10.1007/s11548-010-0413-z
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journal_title：International journal of computer assisted radiology and surgery
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journal_title：International journal of computer assisted radiology and surgery
pub_type： 已发布勘误
doi：10.1007/s11548-020-02119-w
更新日期：2020-03-01 00:00:00
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journal_title：International journal of computer assisted radiology and surgery
pub_type： 杂志文章
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更新日期：2019-03-01 00:00:00
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journal_title：International journal of computer assisted radiology and surgery
pub_type： 杂志文章
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journal_title：International journal of computer assisted radiology and surgery
pub_type： 杂志文章
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更新日期：2016-12-01 00:00:00
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journal_title：International journal of computer assisted radiology and surgery
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journal_title：International journal of computer assisted radiology and surgery
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journal_title：International journal of computer assisted radiology and surgery
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journal_title：International journal of computer assisted radiology and surgery
pub_type： 杂志文章
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journal_title：International journal of computer assisted radiology and surgery
pub_type： 杂志文章
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更新日期：2014-07-01 00:00:00
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journal_title：International journal of computer assisted radiology and surgery
pub_type： 杂志文章
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更新日期：2013-08-20 00:00:00
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journal_title：International journal of computer assisted radiology and surgery
pub_type： 杂志文章
doi：10.1007/s11548-016-1359-6
更新日期：2016-09-01 00:00:00
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journal_title：International journal of computer assisted radiology and surgery
pub_type： 杂志文章
doi：10.1007/s11548-016-1444-x
更新日期：2017-01-01 00:00:00
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journal_title：International journal of computer assisted radiology and surgery
pub_type： 杂志文章
doi：10.1007/s11548-018-1716-8
更新日期：2018-05-01 00:00:00
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journal_title：International journal of computer assisted radiology and surgery
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journal_title：International journal of computer assisted radiology and surgery
pub_type： 杂志文章
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journal_title：International journal of computer assisted radiology and surgery
pub_type： 杂志文章
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更新日期：2014-03-01 00:00:00
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journal_title：International journal of computer assisted radiology and surgery
pub_type： 杂志文章
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更新日期：2017-01-01 00:00:00
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journal_title：International journal of computer assisted radiology and surgery
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journal_title：International journal of computer assisted radiology and surgery
pub_type： 杂志文章
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journal_title：International journal of computer assisted radiology and surgery
pub_type： 杂志文章
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journal_title：International journal of computer assisted radiology and surgery
pub_type： 杂志文章
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journal_title：International journal of computer assisted radiology and surgery
pub_type： 杂志文章
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更新日期：2014-11-01 00:00:00
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journal_title：International journal of computer assisted radiology and surgery
pub_type： 杂志文章
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更新日期：2018-07-01 00:00:00
