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10th 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
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
Manufacturing of Ultrasound- and MRI-Compatible Aortic Valves Using 3D Printing for Analysis and Simulation. 12-21 - Esther Puyol-Antón
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
Comparison of 2D Echocardiography and Cardiac Cine MRI in the Assessment of Regional Left Ventricular Wall Thickness. 52-62 - Zhaohan Xiong
Fully Automatic 3D Bi-Atria Segmentation from Late Gadolinium-Enhanced MRIs Using Double Convolutional Neural Networks. 63-71 - Andriy Myronenko
4D CNN for Semantic Segmentation of Cardiac Volumetric Sequences. 72-80 - Kevin Jamart, Zhaohan Xiong
Two-Stage 2D CNN for Automatic Atrial Segmentation from LGE-MRIs. 81-89 - Sofie Tilborghs
3D Left Ventricular Segmentation from 2D Cardiac MR Images Using Spatial Context. 90-99 - Gaëtan Desrues, Hervé Delingette
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
Learning Interactions Between Cardiac Shape and Deformation: Application to Pulmonary Hypertension. 119-127 - Agisilaos Chartsias, Giorgos Papanastasiou
Multimodal Cardiac Segmentation Using Disentangled Representation Learning. 128-137 - Kobe Bamps
DeepLA: Automated Segmentation of Left Atrium from Interventional 3D Rotational Angiography Using CNN. 138-146 - Yingyu Yang
Non-invasive Pressure Estimation in Patients with Pulmonary Arterial Hypertension: Data-Driven or Model-Based? 147-156 - Xabier Morales
Deep Learning Surrogate of Computational Fluid Dynamics for Thrombus Formation Risk in the Left Atrial Appendage. 157-166 - Alexandre Legay
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
Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training. 186-194
Multi-Sequence Cardiac MR Segmentation Challenge
- Buntheng Ly, Hubert Cochet
Style Data Augmentation for Robust Segmentation of Multi-modality Cardiac MRI. 197-208 - Chen Chen
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
Pseudo-3D Network for Multi-sequence Cardiac MR Segmentation. 237-245 - Xiyue Wang
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
Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI. 290-299 - Sulaiman Vesal, Nishant Ravikumar
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
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
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
Left Ventricle Quantification with Cardiac MRI: Deep Learning Meets Statistical Models of Deformation. 384-394 - Sofie Tilborghs
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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