PMID- 28160219
OWN - NLM
STAT- MEDLINE
DCOM- 20180711
LR  - 20181113
IS  - 1741-0444 (Electronic)
IS  - 0140-0118 (Linking)
VI  - 55
IP  - 9
DP  - 2017 Sep
TI  - Automatic segmentation of left ventricle cavity from short-axis cardiac magnetic 
      resonance images.
PG  - 1563-1577
LID - 10.1007/s11517-017-1614-1 [doi]
AB  - In this paper, a computational framework is proposed to perform a fully automatic 
      segmentation of the left ventricle (LV) cavity from short-axis cardiac magnetic 
      resonance (CMR) images. In the initial phase, the region of interest (ROI) is 
      automatically identified on the first image frame of the CMR slices. This is done 
      by partitioning the image into different regions using a standard fuzzy c-means 
      (FCM) clustering algorithm where the LV region is identified according to its 
      intensity, size and circularity in the image. Next, LV segmentation is performed 
      within the identified ROI by using a novel clustering method that utilizes an 
      objective functional with a dissimilarity measure that incorporates a circular 
      shape function. This circular shape-constrained FCM algorithm is able to 
      differentiate pixels with similar intensity but are located in different regions 
      (e.g. LV cavity and non-LV cavity), thus improving the accuracy of the 
      segmentation even in the presence of papillary muscles. In the final step, the 
      segmented LV cavity is propagated to the adjacent image frame to act as the ROI. 
      The segmentation and ROI propagation are then iteratively executed until the 
      segmentation has been performed for the whole cardiac sequence. Experiment 
      results using the LV Segmentation Challenge validation datasets show that our 
      proposed framework can achieve an average perpendicular distance (APD) shift of 
      2.23 +/- 0.50 mm and the Dice metric (DM) index of 0.89 +/- 0.03, which is comparable 
      to the existing cutting edge methods. The added advantage over state of the art 
      is that our approach is fully automatic, does not need manual initialization and 
      does not require a prior trained model.
FAU - Yang, Xulei
AU  - Yang X
AUID- ORCID: 0000-0002-7002-4564
AD  - Department of Computing Science, Institute of High Performance Computing, A*STAR, 
      1 Fusionopolis Way, Connexis, Singapore, 138632, Singapore. 
      yangx@ihpc.a-star.edu.sg.
FAU - Song, Qing
AU  - Song Q
AD  - School of Electrical and Electronics Engineering, Nanyang Technological 
      University, 50 Nanyang Avenue, Singapore, 639798, Singapore.
FAU - Su, Yi
AU  - Su Y
AD  - Department of Computing Science, Institute of High Performance Computing, A*STAR, 
      1 Fusionopolis Way, Connexis, Singapore, 138632, Singapore.
LA  - eng
GR  - 132 148 0012/Biomedical Engineering Programme (BEP) Grant, A*STAR, Singapore/
PT  - Journal Article
DEP - 20170203
PL  - United States
TA  - Med Biol Eng Comput
JT  - Medical & biological engineering & computing
JID - 7704869
SB  - IM
MH  - Algorithms
MH  - Heart Ventricles/*physiopathology
MH  - Humans
MH  - Image Interpretation, Computer-Assisted/methods
MH  - Magnetic Resonance Imaging/methods
MH  - Pattern Recognition, Automated
OTO - NOTNLM
OT  - Cardiac image segmentation
OT  - Cardiac magnetic resonance imaging
OT  - Circular shape-constrained fuzzy C-means
OT  - Left ventricle
EDAT- 2017/02/06 06:00
MHDA- 2018/07/12 06:00
CRDT- 2017/02/05 06:00
PHST- 2016/04/15 00:00 [received]
PHST- 2017/01/25 00:00 [accepted]
PHST- 2017/02/06 06:00 [pubmed]
PHST- 2018/07/12 06:00 [medline]
PHST- 2017/02/05 06:00 [entrez]
AID - 10.1007/s11517-017-1614-1 [pii]
AID - 10.1007/s11517-017-1614-1 [doi]
PST - ppublish
SO  - Med Biol Eng Comput. 2017 Sep;55(9):1563-1577. doi: 10.1007/s11517-017-1614-1. 
      Epub 2017 Feb 3.