PMID- 28279915
OWN - NLM
STAT- MEDLINE
DCOM- 20180521
LR  - 20181202
IS  - 1361-8423 (Electronic)
IS  - 1361-8415 (Linking)
VI  - 38
DP  - 2017 May
TI  - Low-rank and sparse decomposition based shape model and probabilistic atlas for 
      automatic pathological organ segmentation.
PG  - 30-49
LID - S1361-8415(17)30026-9 [pii]
LID - 10.1016/j.media.2017.02.008 [doi]
AB  - One major limiting factor that prevents the accurate delineation of human organs 
      has been the presence of severe pathology and pathology affecting organ borders. 
      Overcoming these limitations is exactly what we are concerned in this study. We 
      propose an automatic method for accurate and robust pathological organ 
      segmentation from CT images. The method is grounded in the active shape model 
      (ASM) framework. It leverages techniques from low-rank and sparse decomposition 
      (LRSD) theory to robustly recover a subspace from grossly corrupted data. We 
      first present a population-specific LRSD-based shape prior model, called LRSD-SM, 
      to handle non-Gaussian gross errors caused by weak and misleading appearance cues 
      of large lesions, complex shape variations, and poor adaptation to the finer 
      local details in a unified framework. For the shape model initialization, we 
      introduce a method based on patient-specific LRSD-based probabilistic atlas (PA), 
      called LRSD-PA, to deal with large errors in atlas-to-target registration and low 
      likelihood of the target organ. Furthermore, to make our segmentation framework 
      more efficient and robust against local minima, we develop a hierarchical ASM 
      search strategy. Our method is tested on the SLIVER07 database for liver 
      segmentation competition, and ranks 3rd in all the published state-of-the-art 
      automatic methods. Our method is also evaluated on some pathological organs 
      (pathological liver and right lung) from 95 clinical CT scans and its results are 
      compared with the three closely related methods. The applicability of the 
      proposed method to segmentation of the various pathological organs (including 
      some highly severe cases) is demonstrated with good results on both quantitative 
      and qualitative experimentation; our segmentation algorithm can delineate organ 
      boundaries that reach a level of accuracy comparable with those of human raters.
CI  - Copyright (c) 2017 Elsevier B.V. All rights reserved.
FAU - Shi, Changfa
AU  - Shi C
AD  - Mobile E-business Collaborative Innovation Center of Hunan Province, Hunan 
      University of Commerce, Changsha 410205, China; School of Computer Science and 
      Technology, Harbin Institute of Technology, Harbin 150001, China.
FAU - Cheng, Yuanzhi
AU  - Cheng Y
AD  - School of Computer Science and Technology, Harbin Institute of Technology, Harbin 
      150001, China. Electronic address: yzcheng@hitwh.edu.cn.
FAU - Wang, Jinke
AU  - Wang J
AD  - School of Computer Science and Technology, Harbin Institute of Technology, Harbin 
      150001, China.
FAU - Wang, Yadong
AU  - Wang Y
AD  - School of Computer Science and Technology, Harbin Institute of Technology, Harbin 
      150001, China.
FAU - Mori, Kensaku
AU  - Mori K
AD  - Information and Communications Headquarters, Nagoya University, Nagoya 464-8601, 
      Japan.
FAU - Tamura, Shinichi
AU  - Tamura S
AD  - Department of Radiology, Graduate School of Medicine, Osaka University, Suita 
      565-0871, Japan.
LA  - eng
PT  - Journal Article
DEP - 20170222
PL  - Netherlands
TA  - Med Image Anal
JT  - Medical image analysis
JID - 9713490
SB  - IM
MH  - *Algorithms
MH  - Humans
MH  - Liver/*diagnostic imaging/*pathology
MH  - Lung/*diagnostic imaging/*pathology
MH  - Probability
MH  - Tomography, X-Ray Computed/*methods
OTO - NOTNLM
OT  - Active shape model
OT  - Hierarchical model
OT  - Low-rank and sparse decomposition
OT  - Pathological organ segmentation
OT  - Probabilistic atlas
EDAT- 2017/03/11 06:00
MHDA- 2018/05/22 06:00
CRDT- 2017/03/11 06:00
PHST- 2016/04/18 00:00 [received]
PHST- 2017/02/13 00:00 [revised]
PHST- 2017/02/15 00:00 [accepted]
PHST- 2017/03/11 06:00 [pubmed]
PHST- 2018/05/22 06:00 [medline]
PHST- 2017/03/11 06:00 [entrez]
AID - S1361-8415(17)30026-9 [pii]
AID - 10.1016/j.media.2017.02.008 [doi]
PST - ppublish
SO  - Med Image Anal. 2017 May;38:30-49. doi: 10.1016/j.media.2017.02.008. Epub 2017 
      Feb 22.