Accepted posters

Regular papers

  • C Vanelli, W Xia, J Moore, T Peters “How accurate does transesophageal echocardiography identify the mitral valve?"
  • O Al-Kadi, A Lu, A Sinusas, J Duncan “Stochastic Model-Based LV Segmentation in 3D Echocardiography using Fractional Brownian Motion”
  • Y Duan, J Feng, J Lu, J Zhou “Context Aware 3D Fully Convolutional Networks for Coronary Artery Segmentation”
  • E Puyol-Anton, B Ruijsink, H Langet, M Craene, P Piro, J Schnabel, A King “Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas”
  • S Monaci, D Nordsletten, O Aslanidi “Computational Modelling of Electro-Mechanical Coupling in the Atria and its Changes during Atrial Fibrillation”
  • R Attar, M Pereañez, A Gooya, X Alba, L Zhang, SK Piechnik, S Neubauer, SE Petersen, A Frangi “High Throughput Computation of Reference Ranges of Biventricular Cardiac Function on the UK Biobank Population Cohort”
  • Z Li, J Feng, Z Feng, Y An, Y Gao, B Lu, J Zhou “Lumen Segmentation of Aortic Dissection with Cascaded Convolutional Network”
  • J Liu, C Jin, Y Du, J Feng, J Lu, J Zhou “A Vessel-Focused 3D Convolutional Network for Automatic Segmentation and Classification of   Coronary Artery Plaques in Cardiac CTA”
  • A Aly, A Aly, M Elrakhawy, K Haroun, L Prieto-Riascos, RC Gorman Jr, N Yushkevich, Y Saito, JH Gorman III, RC Gorman, P Yushkevich, AM  Pouch “Automated image segmentation of the left ventricular mitral valve complex for ischemic mitral regurgitation”
  • L Li, G Yang, F Wu, T Wong, R Mohiaddin, D Firmin, J Keegan, L Xu, X Zhuang “Atrial scarring segmentation via potential learning in the graph-cut framework”
  • S Yoon, S Baek, D Lee ”4D cardiac motion modeling using pair-wise mesh registration”
  • B Villard, E Zacur, V Grau “ISACHI: Integrated Segmentation and Alignment Correction for Heart Images”
  • D Yang, L Bo, L Axel, D Metaxas “3D LV Probabilistic Segmentation in Cardiac MRI using Generative Adversarial Network”
  • C Wang, T MacGillivray, G Macnaught, G Yang, D Newby ”A Two-stage U-Net Model for 3D Multi-class Segmentation on Full-resolution Cardiac  Data”
  • I Genua, A Olivares, E Silva, J Mill, A Fernandez, A Aguado, M Nuñez, T Potter, X Freixa, O Camara “Centreline-based shape descriptors of the left atrial appendage in relation with thrombus formation”

LV Quantification challenge

  • W Yan, Y Wang, S Chen, R Geest, Q Tao “ESU-P-Net: Cascading Network for Full Quantification of Left Ventricle from Cine MRI”
  • G Yang, T Hua, C Lu, T Pan, X Yang, L Hu, J Wu, X Zhu, H Shu “Left Ventricle Full Quantification via Hierarchical Quantification Network”
  • A Atehortua, M Garreau, D Romo-Bucheli, E Romero “Automatic left ventricle quantification in Cardiac MRI via hierarchical refinement of high-level features by a salient perceptual grouping model”
  • F Guo, M Ng, G Wright “Cardiac MRI Left Ventricle Segmentation and Quantification: A Framework Combining U-net and Continuous Max-flow”
  • J Liu, X Li, H Ren, Q Li “Multi-Estimator Full Left Ventricle Quantification through Ensemble Learning”
  • A Debus, E Ferrante “Left ventricle quantification through spatio-temporal CNNs”
  • Y Jang, S Kim, H Shim, HJ Chang “Full Quantification of Left Ventricle using Deep Multitask Network with Combination of 2D and 3D convolution on 2D+t cine MRI”
  • E Kerfoot, J Clough, I Oksuz, J Lee, AP King, J Schnabel “Left-Ventricle Quantification Using Residual U-Net”
  • J Li, Z Hu “LV full quantification using deep layer aggregation based multitask relationship learning”
  • I Grinias, G Tziritas “Convexity and connectivity principles applied for Left Ventricle segmentation and quantification”
  • H Xu, J Schneider, V Grau “Calculation of anatomical and functional metrics using deep learning in Cardiac MRI”
  • L Liu, J Ma, J Wang, J Xiao “Automated Full Quantification of Left Ventricle using deep neural networks”

3D atrial segmentation challenge

  • CJ Preetha, S Haridasan, V Abdi, S Engelhardt “Segmentation of the Left Atrium from 3D Gd-Enhanced MRI With Convolutional Neural Networks”
  • N Savioli, G Montana, P Lamata “V-FCNN: Volumetric Fully Convolution Neural Network for Automatic Atrial Segmentation”
  • E Fok, J Zhao, J Fernandez “Ensemble of convolutional neural networks for heart segmentation”
  • C Chen, W Bai, D Rueckert “Multi-Task Learning for Left Atrial Segmentation on GE-MRI”
  • M Nuñez, X Zhuang, G Sanroma, L Li, L Xu, C Butakoff, O Camara “Left atrial segmentation combining multi-atlas whole heart labeling and shape-based atlas selection”
  • Y Liu, Y Dai, C Yan, K Wang “Deep Learning Based Method for Left Atrial Segmentation in GE-MRI”
  • S Vesal, N Ravikumar, A Maier “Dilated Convolution in Neural Networks for Left Atrial Segmentation in 3D Late Gadolinium Enhanced-MRI”
  • D Borra, A Masci, L Esposito, A Andalo, C Fabbri, C Corsi “A semantic-wise convolutional neural network approach for 3D left atrium segmentation from LGE-MRI”
  • E Puybareau, Z Zhou, Y Khoudli, Y Xu, J Lacotte, T Géraud “Left Atrial Segmentation in a Few Seconds Using Fully Convolutional Network and Transfer Learning”
  • C Vente, M Veta, O Razeghi, S Niederer, J Pluim, K Rhode, R Karim “Convolutional Neural Networks for Segmentation of the Left Atrium from Gadolinium-Enhancement MR Images”
  • T Sodergren, R Bhalodia, R Whitaker, J Cates, N Marrouche, S Elhabian “Mixture Modeling of Global Shape Priors and Autoencoding Local Intensity Priors for Left Atrium Segmentation”
  • Q Xia, Y Hao, Z Hu, A Hao “Automatic 3D Atrial Segmentation from Gadolinium-enhanced MRI using Volumetric Fully Convolutional Networks”
  • S Jia, A Despinasse, Z Wang, H Delingette, X Pennec, P Jaïs, H Cochet, M Sermesant “Automatically Segmenting the Left Atrium from Cardiac Images Using Successive 3D U-Nets”
  • M Qiao, Y Wang, R Geest, Q Tao ”Fully Automated Left Atrium Cavity Segmentation from 3D GE-MRI by Multi-Atlas Selection and Registration”
  • C Bian, X Yang, S Zheng, J Ma, YA Liu, R Nezafat, PA Heng, Y Zheng “Pyramid Network with online Hard Example Mining for Accurate Left Atrium Segmentation”
  • X Yang, N Wang, Y Wang, X Wang, R Nezafat, D Ni, PA Heng “Combating Uncertainty with Novel Losses for Automatic Atrium Segmentation”
  • C Li, Q Tong, X Liao, W Si, Y Sun, Q Wang, PA Heng “Attention based hierarchical aggregation network for 3D Left atrial segmentation”