Table of Contents

• Co-registered Cardiac ex vivo DT Images and Histological Images for Fibrosis QuantificationPeter Lin, Anne Martel, Susan Camilleri, Mihaela Pop. 3-11 [doi]
• Manufacturing of Ultrasound- and MRI-Compatible Aortic Valves Using 3D Printing for Analysis and SimulationShu Wang, Harminder Gill, Weifeng Wan, Helen Tricker, Joao Filipe Fernandes, Yohan Noh, Sergio Uribe, Jesus Urbina, Julio Sotelo, Ronak Rajani, Pablo Lamata, Kawal S. Rhode. 12-21 [doi]
• Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and Variational AutoencodersEsther Puyol-Antón, Bram Ruijsink, James R. Clough, Ilkay Öksüz, Daniel Rueckert, Reza Razavi, Andrew P. King. 22-30 [doi]
• Ultra-DenseNet for Low-Dose X-Ray Image Denoising in Cardiac Catheter-Based ProceduresYimin Luo, Daniel Toth, Kui Jiang, Kuberan Pushparajah, Kawal Rhode. 31-42 [doi]
• A Cascade Regression Model for Anatomical Landmark DetectionZimeng Tan, Yongjie Duan, Ziyi Wu, Jianjiang Feng, Jie Zhou 0001. 43-51 [doi]
• Comparison of 2D Echocardiography and Cardiac Cine MRI in the Assessment of Regional Left Ventricular Wall ThicknessVera H. J. van Hal, Debbie Zhao, Kathleen Gilbert, Thiranja P. Babarenda Gamage, Charlène Alice Mauger, Robert N. Doughty, Malcolm E. Legget, Jichao Zhao, Aaqel Nalar, Oscar Camara, Alistair A. Young, Vicky Y. Wang, Martyn P. Nash. 52-62 [doi]
• Fully Automatic 3D Bi-Atria Segmentation from Late Gadolinium-Enhanced MRIs Using Double Convolutional Neural NetworksZhaohan Xiong, Aaqel Nalar, Kevin Jamart, Martin K. Stiles, Vadim V. Fedorov, Jichao Zhao. 63-71 [doi]
• 4D CNN for Semantic Segmentation of Cardiac Volumetric SequencesAndriy Myronenko, Dong Yang, Varun Buch, Daguang Xu, Alvin Ihsani, Sean Doyle, Mark Michalski, Neil A. Tenenholtz, Holger Roth. 72-80 [doi]
• Two-Stage 2D CNN for Automatic Atrial Segmentation from LGE-MRIsKevin Jamart, Zhaohan Xiong, Gonzalo D. Maso Talou, Martin K. Stiles, Jichao Zhao. 81-89 [doi]
• Towards Hyper-Reduction of Cardiac Models Using Poly-affine TransformationsGaëtan Desrues, Hervé Delingette, Maxime Sermesant. 100-108 [doi]
• Conditional Generative Adversarial Networks for the Prediction of Cardiac Contraction from Individual FramesJulius Ossenberg-Engels, Vicente Grau. 109-118 [doi]
• Learning Interactions Between Cardiac Shape and Deformation: Application to Pulmonary HypertensionMaxime Di Folco, Patrick Clarysse, Pamela Moceri, Nicolas Duchateau. 119-127 [doi]
• Multimodal Cardiac Segmentation Using Disentangled Representation LearningAgisilaos Chartsias, Giorgos Papanastasiou, Chengjia Wang, Colin Stirrat, Scott Semple, David E. Newby, Rohan Dharmakumar, Sotirios A. Tsaftaris. 128-137 [doi]
• DeepLA: Automated Segmentation of Left Atrium from Interventional 3D Rotational Angiography Using CNNKobe Bamps, Stijn De Buck, Jeroen Bertels, Rik Willems, Christophe Garweg, Peter Haemers, Joris Ector. 138-146 [doi]
• Non-invasive Pressure Estimation in Patients with Pulmonary Arterial Hypertension: Data-Driven or Model-Based?Yingyu Yang, Stephane Gillon, Jaume Banus, Pamela Moceri, Maxime Sermesant. 147-156 [doi]
• Deep Learning Surrogate of Computational Fluid Dynamics for Thrombus Formation Risk in the Left Atrial AppendageXabier Morales, Jordi Mill, Kristine A. Juhl, Andy L. Olivares, Guillermo Jiménez-Pérez, Rasmus R. Paulsen, Oscar Camara. 157-166 [doi]
• End-to-end Cardiac Ultrasound Simulation for a Better Understanding of Image QualityAlexandre Legay, Thomas Tiennot, Jean-François Gelly, Maxime Sermesant, Jean Bulté. 167-175 [doi]
• Probabilistic Motion Modeling from Medical Image Sequences: Application to Cardiac Cine-MRIJulian Krebs, Tommaso Mansi, Nicholas Ayache, Hervé Delingette. 176-185 [doi]
• Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised TrainingHuaqi Qiu, Chen Qin, Loïc Le Folgoc, Benjamin Hou, Jo Schlemper, Daniel Rueckert. 186-194 [doi]
• Style Data Augmentation for Robust Segmentation of Multi-modality Cardiac MRIBuntheng Ly, Hubert Cochet, Maxime Sermesant. 197-208 [doi]
• Unsupervised Multi-modal Style Transfer for Cardiac MR SegmentationChen Chen 0042, Cheng Ouyang, Giacomo Tarroni, Jo Schlemper, Huaqi Qiu, Wenjia Bai, Daniel Rueckert. 209-219 [doi]
• An Automatic Cardiac Segmentation Framework Based on Multi-sequence MR ImageYashu Liu 0003, Wei Wang 0169, Kuanquan Wang, Chengqin Ye, Gongning Luo. 220-227 [doi]
• Cardiac Segmentation of LGE MRI with Noisy LabelsHolger Roth, Wentao Zhu 0001, Dong Yang, Ziyue Xu, Daguang Xu. 228-236 [doi]
• Pseudo-3D Network for Multi-sequence Cardiac MR SegmentationTao Liu, Yun Tian, Shifeng Zhao, Xiaoying Huang, Yang Xu, Gaoyuan Jiang, Qingjun Wang. 237-245 [doi]
• SK-Unet: An Improved U-Net Model with Selective Kernel for the Segmentation of Multi-sequence Cardiac MRXiyue Wang, Sen Yang, Mingxuan Tang, Yunpeng Wei, Xiao Han, Ling He, Jing Zhang. 246-253 [doi]
• Multi-sequence Cardiac MR Segmentation with Adversarial Domain Adaptation NetworkJiexiang Wang, Hongyu Huang, Chaoqi Chen, Wenao Ma, Yue Huang 0001, Xinghao Ding. 254-262 [doi]
• Deep Learning Based Multi-modal Cardiac MR Image SegmentationRencheng Zheng, Xingzhong Zhao, Xingming Zhao, He Wang. 263-270 [doi]
• Segmentation of Multimodal Myocardial Images Using Shape-Transfer GANXumin Tao, Hongrong Wei, Wufeng Xue, Dong Ni. 271-279 [doi]
• Knowledge-Based Multi-sequence MR Segmentation via Deep Learning with a Hybrid U-Net++ ModelJinchang Ren, He Sun, Yumin Huang, Hao Gao. 280-289 [doi]
• Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRIVíctor M. Campello, Carlos Martín-Isla, Cristian Izquierdo, Steffen E. Petersen, Miguel Ángel González Ballester, Karim Lekadir. 290-299 [doi]
• Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain AdaptationSulaiman Vesal, Nishant Ravikumar, Andreas Maier. 300-308 [doi]
• A Two-Stage Fully Automatic Segmentation Scheme Using Both 2D and 3D U-Net for Multi-sequence Cardiac MRHaohao Xu, Zhuangwei Xu, Wenting Gu, Qi Zhang. 309-316 [doi]
• Adversarial Convolutional Networks with Weak Domain-Transfer for Multi-sequence Cardiac MR Images SegmentationJingkun Chen, Hongwei Li 0004, Jianguo Zhang, Bjoern H. Menze. 317-325 [doi]
• Best (and Worst) Practices for Organizing a Challenge on Cardiac Biophysical Models During AI Summer: The CRT-EPiggy19 ChallengeOscar Camara. 329-341 [doi]
• Prediction of CRT Activation Sequence by Personalization of Biventricular Models from Electroanatomical MapsJuan Francisco Gomez, Beatriz Trénor, Rafael Sebastian. 342-351 [doi]
• Prediction of CRT Response on Personalized Computer ModelsSvyatoslav Khamzin, Arsenii Dokuchaev, Olga Solovyova. 352-363 [doi]
• Eikonal Model Personalisation Using Invasive Data to Predict Cardiac Resynchronisation Therapy Electrophysiological ResponseNicolas Cedilnik, Maxime Sermesant. 364-372 [doi]
• Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNsNils Gessert, Alexander Schlaefer. 375-383 [doi]
• Left Ventricle Quantification with Cardiac MRI: Deep Learning Meets Statistical Models of DeformationJorge Corral Acero, Hao Xu, Ernesto Zacur, Jürgen E. Schneider, Pablo Lamata, Alfonso Bueno-Orovio, Vicente Grau. 384-394 [doi]
• A Two-Stage Temporal-Like Fully Convolutional Network Framework for Left Ventricle Segmentation and Quantification on MR ImagesZhou Zhao, Nicolas Boutry, Élodie Puybareau, Thierry Géraud. 405-413 [doi]