These are raters who participated in the original STACOM 2011 workshop. The first consensus was established based on these raters.
Contour-constrained optical flow tracking (AO)
This method involves manual drawing at the first frame before automatically tracks contour in the subsequent frames by minimizing energy functionals, which consist of optical flow and contour properties constraints.
Submitted by the Nile University, Cairo, Egypt.
Ahmed S. Fahmy, Ahmed O. Al-Agamy, and Ayman Khalifa, "Myocardial Segmentation Using Contour-Constrained Optical Flow Tracking", STACOM 2011, LNCS 7085, pp 120-128, Springer, 2012.
Manually guide-point modeling assisted fitting of cardiac model (AU)
This is an expert-guided segmentation method where a finite element model of the LV was fitted interactively throughout the cardiac frames.
Submitted by the University of Auckland, New Zealand.
Bo Li, Yingmin Liu, Christopher J. Occleshaw, Brett R. Cowan, and Alistair A. Young, "In-line Automated Tracking for Ventricular Function With Magnetic Resonance Imaging", JACC: Cardiovascular Imaging, 3:8, pp 860-866, 2010.
Block matching algorithm (DS)
This method involves manual drawing at the first frame and the subsequent myocardial contours were detected by using the block-matching technique.
Submitted by Diagnostic Inc.
S. Ourselin, A. Roche, S. Prima, and N. Ayache, "Block Matching: A General Framework to Improve Robustness of Rigid Registration of Medical Images", MICCAI 2000, LNCS 1935, pp 557-566, Springer, 2000.
Layered spatio-temporal forests algorithm (INR)
This segmentation algorithm was based on two layers of spatio-temporal decision forests with almost no assumptions on the data nor explicitly specifying the segmentation rules. 4D spatio-temporal features to classification with decision forests were introduced in the first layer for context aware MR intensity standardization and image alignment. The second layer was then used for the final image segmentation. This is a fully automated segmentation algorithm without any user intervention.
Submitted by INRIA, France.
Ján Margeta, Ezequiel Geremia, Antonio Criminisi, and Nicholas Ayache, "Layered Spatio-temporal Forests for Left Ventricle Segmentation from 4D Cardiac MRI Data", STACOM 2011, LNCS 7085, pp 109-119, Springer, 2011.
Deformable registration method (SCR)
This fully automated algorithm registered and segmented cardiac MR images based on inverse consistency of deformable registration to register all frames to the first frame. The segmentation was applied to any frame and propagated to any other frames in the sequence through forward and backward deformation fields.
Submitted by Siemens Corporation Research group.
Marie-Pierre Jolly, Christoph Guetter, Xiaoguang Lu, Hui Xue, and Jens Guehring, "Automatic Segmentation of the Myocardium in Cine MR Images Using Deformable Registration", STACOM 2011, LNCS 7085, pp 98-108, 2011.
Raters who have submitted and provided us their algorithm description.
A fully Convolutional Neural Network (VT)
This method was based on a 15 layers of Convolutional Neural Network (CNN) for segmentation. The network was trained for a pixel-wise labelling. Code is available from a github page.
Submitted by the Booz Allen Hamilton, USA.
Phi Vu Tran, "A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI", arXiv:1604.00494 [cs.CV], 2017.
Convolutional neural network regression (TLK)
This method was based on Convolutional Neural Network (CNN) regression by utilising the radial distance of the LV walls to segment the myocardium. Two CNNs were used: 1) a network to detect the centre of the ventricular cavity point, and 2) a network to determine the radial distances from the centre point.
Submitted by the University of Malaya, Malaysia.
Li Kuo Tan, Yih Miin Liew, EinlyLim, and Robert A. McLaughlin, "Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine MR sequences", Medical Image Analysis, 39, pp 78-86, 2017.
Histogram of Oriented Gradients (SG)
This fully automated segmentation algorithm was based on the global control of a statistical deformable model with local control assumption of the material properties of non-rigid objects. The initial 3D model was estimated using a Histogram of Oriented Gradients. Non-rigid motion was reconstructed using a variational method.
Submitted by the University of California in Los Angeles, USA.
Sharath K Gopal, "Unified Deterministic/Statistical Deformable Models for Cardiac Image Analysis", PhD Thesis Dissertation, UCLA, 2016.
Participants who are still working on developing the segmentation algorithms and yet to submit their segmentation results.
|Huafei Hu||University of Sheffield, UK||submitted|
|Tan Li Kuo||University of Malaya, Kuala Lumpur||submitted|
|Phi Vu Tran||Booz Allen Hamilton, USA||submitted (2 versions)|
|Sharath Gopal||University of California, Los Angeles, USA||submitted|
|Oliver Moolan-Feroze||Bristol University, UK||submitted|
|Kumaradevan Punithakumar||University of Alberta, Canada||not yet|
|Mahapatra Dwarikanath||ETH Zurich, Switzerland||not yet|
|Angélica Atehortúa||Universidad Nacional, Bogota, Colombia||not yet|
|Mahdi Hajiaghayi||University of California, Irvine, USA||unreachable|
|Bogdan Budescu||Transilvania University, Brasov, Romania||not yet|
|Zhijie Wang||Western University, Ontario, Canada||not yet|
|Xiahai Zhuang||Shanghai Advanced Research Institute, China||not yet|
|Yang Yu||Rutgers University, USA||not yet|
|Moslem Avendi||UC Irvine, USA||not yet|
|Lichao Wang||TU Munich, German||not yet|
|Cristian Linte||Rochester Institute of Technology, USA||not yet|
|Wenjun Tan||Northeastern University, China||not yet|
|Martin Rajchi||Imperial College London, UK||not yet|
|Wendeson da Silva Oliveira||Federal University of Pernambuco, Brazil||not yet|
|Joyce Teixeira||Federal University of Pernambuco, Brazil||not yet|
|Piere-Marc Jodoin||University of Sherbrooke, Canada||not yet|
|Gongning Luo||Harbin Institute of Technology, China||not yet|
|Yurun Ma||Lanzhou University, China||not yet|
|Maimoona Khan||National University of Sciences and Technology, Pakistan||not yet|
|Yang Xulei||Institute of High Performance Computing, A*STAR, Singapore||not yet|
|Heran Yang||Xi'an Jiaotong University, China||not yet|
|Gustavo Canavaci Barizon||University of Säo Paolo, Brazil||not yet|
|Ariel Hernán Curiale||Universidad Nacional de Cuyo, Argentina||not yet|
|Fan Yang||Guizhou Medical University, China||not yet|
|Brian Rice||NeoSoft Medical, USA||not yet|
|Hassan Mohy-ud-Din||Yale University, USA||not yet|
|Liset Romaguera||Universidad Federal Do Amazonas, Brazil||not yet|
|Aliasghar Mortazi||University of Central Florida, USA||not yet|
|Nasrin Bastani||Isfahan University of Medical Science, Iran||not yet|
|Shehzaad Dhuliawala||University of Massachusetts, USA||September 2017|
|Mobarakol Islam||National University of Singapore||February 2017|
|Mo Yuanhan||Imperial College London, UK||End of 2017|
|Mahendra Khened||Indian Institute of Technology, Madras, India||Oct 2017|
|Awais Muhammad Lodhi||University of Central Punjab, Pakistan||Nov 2017|
|Lv Xuyang||Northeastern University, China||Aug 2017|
|Manuel Morales||MIT, USA||Sep 2017|
|Defeng Chen||Capital Normal University, China||Sep 2017|
|Mohit Pandey||Cornell University, USA||July 2018|
|Xiaoming Liu||Wuhan University of Science and Technology, China||-|
|Xuan Yang||Shenzhen University, China||May 2018|
|Heming Yao||University of Michigan, USA||Nov 2018|
|Xinyi Li||Leiden University Medical Center, the Netherlands||Feb 2018|
|Bradley Kenstler||University of San Fransisco, USA||Sep 2017|
|Rhodri Davies||Barts Heart Centre, UK||Dec 2017|
|Antong Chen||Merc & Co. Inc., USA||Dec 2018|
|Yongkui Xiang||Shenzhen University, China||-|
|Agisilaos Chartsias||University of Edinburgh, UK||2018|