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ORCID10th STACOM@MICCAI 2019: Shenzhen, China
- Mihaela Pop, Maxime Sermesant, Oscar Camara, Xiahai Zhuang, Shuo Li, Alistair A. Young, Tommaso Mansi, Avan Suinesiaputra:
Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Revised Selected Papers. Lecture Notes in Computer Science 12009, Springer 2020, ISBN 978-3-030-39073-0
Regular Papers
- Peter Lin
, Anne L. Martel, Susan Camilleri, Mihaela Pop:
Co-registered Cardiac ex vivo DT Images and Histological Images for Fibrosis Quantification. 3-11 - Shu Wang, Harminder Gill, Weifeng Wan, Helen Tricker, João Filipe Fernandes, Yohan Noh, Sergio Uribe, Jesús Urbina, Julio Sotelo, Ronak Rajani, Pablo Lamata, Kawal S. Rhode:
Manufacturing of Ultrasound- and MRI-Compatible Aortic Valves Using 3D Printing for Analysis and Simulation. 12-21 - Esther Puyol-Antón, Bram Ruijsink, James R. Clough, Ilkay Öksüz, Daniel Rueckert, Reza Razavi, Andrew P. King:
Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and Variational Autoencoders. 22-30 - Yimin Luo, Daniel Toth, Kui Jiang, Kuberan Pushparajah, Kawal S. Rhode:
Ultra-DenseNet for Low-Dose X-Ray Image Denoising in Cardiac Catheter-Based Procedures. 31-42 - Zimeng Tan, Yongjie Duan, Ziyi Wu, Jianjiang Feng, Jie Zhou:
A Cascade Regression Model for Anatomical Landmark Detection. 43-51 - Vera 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:
Comparison of 2D Echocardiography and Cardiac Cine MRI in the Assessment of Regional Left Ventricular Wall Thickness. 52-62 - Zhaohan Xiong, Aaqel Nalar, Kevin Jamart, Martin K. Stiles, Vadim V. Fedorov, Jichao Zhao:
Fully Automatic 3D Bi-Atria Segmentation from Late Gadolinium-Enhanced MRIs Using Double Convolutional Neural Networks. 63-71 - Andriy Myronenko, Dong Yang, Varun Buch, Daguang Xu, Alvin Ihsani, Sean Doyle, Mark Michalski, Neil A. Tenenholtz, Holger Roth:
4D CNN for Semantic Segmentation of Cardiac Volumetric Sequences. 72-80 - Kevin Jamart, Zhaohan Xiong, Gonzalo D. Maso Talou, Martin K. Stiles, Jichao Zhao:
Two-Stage 2D CNN for Automatic Atrial Segmentation from LGE-MRIs. 81-89 - Sofie Tilborghs, Tom Dresselaers, Piet Claus, Jan Bogaert, Frederik Maes:
3D Left Ventricular Segmentation from 2D Cardiac MR Images Using Spatial Context. 90-99 - Gaëtan Desrues, Hervé Delingette, Maxime Sermesant:
Towards Hyper-Reduction of Cardiac Models Using Poly-affine Transformations. 100-108 - Julius Ossenberg-Engels, Vicente Grau:
Conditional Generative Adversarial Networks for the Prediction of Cardiac Contraction from Individual Frames. 109-118 - Maxime Di Folco, Patrick Clarysse, Pamela Moceri, Nicolas Duchateau:
Learning Interactions Between Cardiac Shape and Deformation: Application to Pulmonary Hypertension. 119-127 - Agisilaos Chartsias, Giorgos Papanastasiou, Chengjia Wang, Colin Stirrat, Scott Semple, David E. Newby, Rohan Dharmakumar, Sotirios A. Tsaftaris:
Multimodal Cardiac Segmentation Using Disentangled Representation Learning. 128-137 - Kobe Bamps, Stijn De Buck, Jeroen Bertels, Rik Willems, Christophe Garweg, Peter Haemers, Joris Ector:
DeepLA: Automated Segmentation of Left Atrium from Interventional 3D Rotational Angiography Using CNN. 138-146 - Yingyu Yang, Stephane Gillon, Jaume Banus, Pamela Moceri, Maxime Sermesant:
Non-invasive Pressure Estimation in Patients with Pulmonary Arterial Hypertension: Data-Driven or Model-Based? 147-156 - Xabier Morales, Jordi Mill, Kristine A. Juhl, Andy L. Olivares, Guillermo Jiménez-Pérez, Rasmus R. Paulsen, Oscar Camara:
Deep Learning Surrogate of Computational Fluid Dynamics for Thrombus Formation Risk in the Left Atrial Appendage. 157-166 - Alexandre Legay, Thomas Tiennot, Jean-François Gelly, Maxime Sermesant, Jean Bulté:
End-to-end Cardiac Ultrasound Simulation for a Better Understanding of Image Quality. 167-175 - Julian Krebs, Tommaso Mansi, Nicholas Ayache, Hervé Delingette:
Probabilistic Motion Modeling from Medical Image Sequences: Application to Cardiac Cine-MRI. 176-185 - Huaqi Qiu, Chen Qin, Loïc Le Folgoc, Benjamin Hou, Jo Schlemper, Daniel Rueckert:
Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training. 186-194
Multi-Sequence Cardiac MR Segmentation Challenge
- Buntheng Ly, Hubert Cochet, Maxime Sermesant:
Style Data Augmentation for Robust Segmentation of Multi-modality Cardiac MRI. 197-208 - Chen Chen, Cheng Ouyang, Giacomo Tarroni, Jo Schlemper, Huaqi Qiu, Wenjia Bai, Daniel Rueckert:
Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation. 209-219 - Yashu Liu, Wei Wang, Kuanquan Wang, Chengqin Ye, Gongning Luo:
An Automatic Cardiac Segmentation Framework Based on Multi-sequence MR Image. 220-227 - Holger Roth, Wentao Zhu, Dong Yang, Ziyue Xu, Daguang Xu:
Cardiac Segmentation of LGE MRI with Noisy Labels. 228-236 - Tao Liu, Yun Tian, Shifeng Zhao, Xiaoying Huang, Yang Xu, Gaoyuan Jiang, Qingjun Wang:
Pseudo-3D Network for Multi-sequence Cardiac MR Segmentation. 237-245 - Xiyue Wang, Sen Yang, Mingxuan Tang, Yunpeng Wei, Xiao Han, Ling He, Jing Zhang:
SK-Unet: An Improved U-Net Model with Selective Kernel for the Segmentation of Multi-sequence Cardiac MR. 246-253 - Jiexiang Wang, Hongyu Huang, Chaoqi Chen, Wenao Ma, Yue Huang, Xinghao Ding:
Multi-sequence Cardiac MR Segmentation with Adversarial Domain Adaptation Network. 254-262 - Rencheng Zheng, Xingzhong Zhao, Xingming Zhao, He Wang:
Deep Learning Based Multi-modal Cardiac MR Image Segmentation. 263-270 - Xumin Tao, Hongrong Wei, Wufeng Xue, Dong Ni:
Segmentation of Multimodal Myocardial Images Using Shape-Transfer GAN. 271-279 - Jinchang Ren, He Sun, Yumin Huang, Hao Gao:
Knowledge-Based Multi-sequence MR Segmentation via Deep Learning with a Hybrid U-Net++ Model. 280-289 - Víctor M. Campello, Carlos Martín-Isla, Cristian Izquierdo, Steffen E. Petersen, Miguel Ángel González Ballester, Karim Lekadir:
Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI. 290-299 - Sulaiman Vesal, Nishant Ravikumar, Andreas Maier:
Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation. 300-308 - Haohao Xu, Zhuangwei Xu, Wenting Gu, Qi Zhang:
A Two-Stage Fully Automatic Segmentation Scheme Using Both 2D and 3D U-Net for Multi-sequence Cardiac MR. 309-316 - Jingkun Chen, Hongwei Li, Jianguo Zhang, Bjoern H. Menze:
Adversarial Convolutional Networks with Weak Domain-Transfer for Multi-sequence Cardiac MR Images Segmentation. 317-325
CRT-EPiggy19 Challenge
- Oscar Camara:
Best (and Worst) Practices for Organizing a Challenge on Cardiac Biophysical Models During AI Summer: The CRT-EPiggy19 Challenge. 329-341 - Juan Francisco Gomez, Beatriz Trénor, Rafael Sebastián:
Prediction of CRT Activation Sequence by Personalization of Biventricular Models from Electroanatomical Maps. 342-351 - Svyatoslav Khamzin, Arsenii Dokuchaev, Olga Solovyova:
Prediction of CRT Response on Personalized Computer Models. 352-363 - Nicolas Cedilnik, Maxime Sermesant:
Eikonal Model Personalisation Using Invasive Data to Predict Cardiac Resynchronisation Therapy Electrophysiological Response. 364-372
LV-Full Quantification Challenge
- Nils Gessert, Alexander Schlaefer:
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs. 375-383 - Jorge Corral Acero, Hao Xu, Ernesto Zacur, Jürgen E. Schneider, Pablo Lamata, Alfonso Bueno-Orovio, Vicente Grau:
Left Ventricle Quantification with Cardiac MRI: Deep Learning Meets Statistical Models of Deformation. 384-394 - Sofie Tilborghs, Frederik Maes:
Left Ventricular Parameter Regression from Deep Feature Maps of a Jointly Trained Segmentation CNN. 395-404 - Zhou Zhao, Nicolas Boutry, Élodie Puybareau, Thierry Géraud:
A Two-Stage Temporal-Like Fully Convolutional Network Framework for Left Ventricle Segmentation and Quantification on MR Images. 405-413
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