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ORCID9th STACOM@MICCAI 2018: Granada, Spain
- Mihaela Pop, Maxime Sermesant, Jichao Zhao, Shuo Li, Kristin McLeod, Alistair A. Young, Kawal S. Rhode, Tommaso Mansi:
Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges - 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers. Lecture Notes in Computer Science 11395, Springer 2019, ISBN 978-3-030-12028-3
Regular Papers
- Tabish A. Syed, Babak Samari, Kaleem Siddiqi
:
Estimating Sheets in the Heart Wall. 3-11 - Abhirup Banerjee, Rajesh K. Kharbanda, Robin Choudhury, Vicente Grau:
Automated Motion Correction and 3D Vessel Centerlines Reconstruction from Non-simultaneous Angiographic Projections. 12-20 - Shusil Dangi, Ziv Yaniv, Cristian A. Linte:
Left Ventricle Segmentation and Quantification from Cardiac Cine MR Images via Multi-task Learning. 21-31 - Jakob M. Slipsager, Kristine A. Juhl, Per E. Sigvardsen, Klaus F. Kofoed, Ole De Backer, Andy L. Olivares, Oscar Camara, Rasmus R. Paulsen:
Statistical Shape Clustering of Left Atrial Appendages. 32-39 - Hakim Fadil, John J. Totman, Stéphanie Marchesseau:
Deep Learning Segmentation of the Left Ventricle in Structural CMR: Towards a Fully Automatic Multi-scan Analysis. 40-48 - Fumin Guo, Mengyuan Li, Matthew Ng, Graham A. Wright, Mihaela Pop:
Cine and Multicontrast Late Enhanced MRI Registration for 3D Heart Model Construction. 49-57 - Jordi Mill, Andy L. Olivares, Etelvino Silva, Ibai Genua, Álvaro Fernández, Ainhoa Aguado, Marta Nuñez Garcia, Tom de Potter, Xavier Freixa, Oscar Camara:
Joint Analysis of Personalized In-Silico Haemodynamics and Shape Descriptors of the Left Atrial Appendage. 58-66 - Claire Vannelli, Wenyao Xia, John Moore, Terry M. Peters:
How Accurately Does Transesophageal Echocardiography Identify the Mitral Valve? 67-76 - Omar Sultan Al-Kadi, Allen Lu, Albert J. Sinusas, James S. Duncan:
Stochastic Model-Based Left Ventricle Segmentation in 3D Echocardiography Using Fractional Brownian Motion. 77-84 - Yongjie Duan, Jianjiang Feng, Jiwen Lu, Jie Zhou:
Context Aware 3D Fully Convolutional Networks for Coronary Artery Segmentation. 85-93 - Esther Puyol-Antón, Bram Ruijsink, Hélène Langet, Mathieu De Craene, Paolo Piro, Julia A. Schnabel, Andrew P. King:
Learning Associations Between Clinical Information and Motion-Based Descriptors Using a Large Scale MR-derived Cardiac Motion Atlas. 94-102 - Sofia Monaci, David Nordsletten, Oleg V. Aslanidi:
Computational Modelling of Electro-Mechanical Coupling in the Atria and Its Changes During Atrial Fibrillation. 103-113 - Rahman Attar, Marco Pereañez, Ali Gooya, Xènia Albà, Le Zhang, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Alejandro F. Frangi:
High Throughput Computation of Reference Ranges of Biventricular Cardiac Function on the UK Biobank Population Cohort. 114-121 - Ziyan Li, Jianjiang Feng, Zishun Feng, Yunqiang An, Yang Gao, Bin Lu, Jie Zhou:
Lumen Segmentation of Aortic Dissection with Cascaded Convolutional Network. 122-130 - Jiang Liu, Cheng Jin, Jianjiang Feng, Yubo Du, Jiwen Lu, Jie Zhou:
A Vessel-Focused 3D Convolutional Network for Automatic Segmentation and Classification of Coronary Artery Plaques in Cardiac CTA. 131-141 - Ahmed H. Aly, Abdullah H. Aly, Mahmoud Elrakhawy, Kirlos Haroun, Luis Prieto-Riascos, Robert C. Gorman Jr., Natalie Yushkevich, Yoshiaki Saito, Joseph H. Gorman III, Robert C. Gorman, Paul A. Yushkevich, Alison M. Pouch:
Semi-automated Image Segmentation of the Midsystolic Left Ventricular Mitral Valve Complex in Ischemic Mitral Regurgitation. 142-151 - Lei Li, Guang Yang, Fuping Wu, Tom Wong, Raad Mohiaddin, David N. Firmin, Jenny Keegan, Lingchao Xu, Xiahai Zhuang:
Atrial Scar Segmentation via Potential Learning in the Graph-Cut Framework. 152-160 - Siyeop Yoon, Stephen Baek, Deukhee Lee:
4D Cardiac Motion Modeling Using Pair-Wise Mesh Registration. 161-170 - Benjamin Villard, Ernesto Zacur, Vicente Grau:
ISACHI: Integrated Segmentation and Alignment Correction for Heart Images. 171-180 - Dong Yang, Bo Liu, Leon Axel, Dimitris N. Metaxas:
3D LV Probabilistic Segmentation in Cardiac MRI Using Generative Adversarial Network. 181-190 - Chengjia Wang, Tom J. MacGillivray, Gillian Macnaught, Guang Yang, David E. Newby:
A Two-Stage U-Net Model for 3D Multi-class Segmentation on Full-Resolution Cardiac Data. 191-199 - Ibai Genua, Andy L. Olivares, Etelvino Silva, Jordi Mill, Álvaro Fernández, Ainhoa Aguado, Marta Nuñez Garcia, Tom de Potter, Xavier Freixa, Oscar Camara:
Centreline-Based Shape Descriptors of the Left Atrial Appendage in Relation with Thrombus Formation. 200-208
3D Atrial Segmentation Challenge
- Qing Xia, Yuxin Yao, Zhiqiang Hu, Aimin Hao:
Automatic 3D Atrial Segmentation from GE-MRIs Using Volumetric Fully Convolutional Networks. 211-220 - Shuman Jia, Antoine Despinasse, Zihao Wang, Hervé Delingette, Xavier Pennec, Pierre Jaïs, Hubert Cochet, Maxime Sermesant:
Automatically Segmenting the Left Atrium from Cardiac Images Using Successive 3D U-Nets and a Contour Loss. 221-229 - Mengyun Qiao, Yuanyuan Wang, Rob J. van der Geest, Qian Tao:
Fully Automated Left Atrium Cavity Segmentation from 3D GE-MRI by Multi-atlas Selection and Registration. 230-236 - Cheng Bian, Xin Yang, Jianqiang Ma, Shen Zheng, Yu-An Liu, Reza Nezafat, Pheng-Ann Heng, Yefeng Zheng:
Pyramid Network with Online Hard Example Mining for Accurate Left Atrium Segmentation. 237-245 - Xin Yang, Na Wang, Yi Wang, Xu Wang, Reza Nezafat, Dong Ni, Pheng-Ann Heng:
Combating Uncertainty with Novel Losses for Automatic Left Atrium Segmentation. 246-254 - Caizi Li, Qianqian Tong, Xiangyun Liao, Weixin Si, Yinzi Sun, Qiong Wang, Pheng-Ann Heng:
Attention Based Hierarchical Aggregation Network for 3D Left Atrial Segmentation. 255-264 - Chandrakanth Jayachandran Preetha, Shyamalakshmi Haridasan, Vahid Abdi, Sandy Engelhardt:
Segmentation of the Left Atrium from 3D Gadolinium-Enhanced MR Images with Convolutional Neural Networks. 265-272 - Nicoló Savioli, Giovanni Montana, Pablo Lamata:
V-FCNN: Volumetric Fully Convolution Neural Network for Automatic Atrial Segmentation. 273-281 - Wilson Fok, Kevin Jamart, Jichao Zhao, Justin Fernandez:
Ensemble of Convolutional Neural Networks for Heart Segmentation. 282-291 - Chen Chen, Wenjia Bai, Daniel Rueckert:
Multi-task Learning for Left Atrial Segmentation on GE-MRI. 292-301 - Marta Nuñez Garcia, Xiahai Zhuang, Gerard Sanroma, Lei Li, Lingchao Xu, Constantine Butakoff, Oscar Camara:
Left Atrial Segmentation Combining Multi-atlas Whole Heart Labeling and Shape-Based Atlas Selection. 302-310 - Yashu Liu, Yangyang Dai, Cong Yan, Kuanquan Wang:
Deep Learning Based Method for Left Atrial Segmentation in GE-MRI. 311-318 - Sulaiman Vesal, Nishant Ravikumar, Andreas Maier:
Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI. 319-328 - Davide Borra, Alessandro Masci, Lorena Esposito, Alice Andalò, Claudio Fabbri, Cristiana Corsi:
A Semantic-Wise Convolutional Neural Network Approach for 3-D Left Atrium Segmentation from Late Gadolinium Enhanced Magnetic Resonance Imaging. 329-338 - Élodie Puybareau, Zhou Zhao, Younes Khoudli, Edwin Carlinet, Yongchao Xu, Jérôme Lacotte, Thierry Géraud:
Left Atrial Segmentation in a Few Seconds Using Fully Convolutional Network and Transfer Learning. 339-347 - Coen de Vente, Mitko Veta, Orod Razeghi, Steven A. Niederer, Josien P. W. Pluim, Kawal S. Rhode, Rashed Karim:
Convolutional Neural Networks for Segmentation of the Left Atrium from Gadolinium-Enhancement MRI Images. 348-356 - Tim Sodergren, Riddhish Bhalodia, Ross T. Whitaker, Joshua Cates, Nassir Marrouche, Shireen Y. Elhabian:
Mixture Modeling of Global Shape Priors and Autoencoding Local Intensity Priors for Left Atrium Segmentation. 357-367
Left Ventricle Full Quantification Challenge
- Eric Kerfoot, James R. Clough, Ilkay Öksüz, Jack Lee, Andrew P. King, Julia A. Schnabel:
Left-Ventricle Quantification Using Residual U-Net. 371-380 - Jiahui Li, Zhiqiang Hu:
Left Ventricle Full Quantification Using Deep Layer Aggregation Based Multitask Relationship Learning. 381-388 - Elias Grinias, Georgios Tziritas:
Convexity and Connectivity Principles Applied for Left Ventricle Segmentation and Quantification. 389-401 - Hao Xu, Jürgen E. Schneider, Vicente Grau:
Calculation of Anatomical and Functional Metrics Using Deep Learning in Cardiac MRI: Comparison Between Direct and Segmentation-Based Estimation. 402-411 - Lihong Liu, Jin Ma, Jianzong Wang, Jing Xiao:
Automated Full Quantification of Left Ventricle with Deep Neural Networks. 412-420 - Wenjun Yan, Yuanyuan Wang, Shaoxiang Chen, Rob J. van der Geest, Qian Tao:
ESU-P-Net: Cascading Network for Full Quantification of Left Ventricle from Cine MRI. 421-428 - Guanyu Yang, Tiancong Hua, Chao Lu, Tan Pan, Xiao Yang, Liyu Hu, Jiasong Wu, Xiaomei Zhu, Huazhong Shu:
Left Ventricle Full Quantification via Hierarchical Quantification Network. 429-438 - Angélica Atehortúa, Mireille Garreau, David Romo-Bucheli, Eduardo Romero:
Automatic Left Ventricle Quantification in Cardiac MRI via Hierarchical Refinement of High-Level Features by a Salient Perceptual Grouping Model. 439-449 - Fumin Guo, Matthew Ng, Graham A. Wright:
Cardiac MRI Left Ventricle Segmentation and Quantification: A Framework Combining U-Net and Continuous Max-Flow. 450-458 - Jiasha Liu, Xiang Li, Hui Ren, Quanzheng Li:
Multi-estimator Full Left Ventricle Quantification Through Ensemble Learning. 459-465 - Alejandro Debus, Enzo Ferrante:
Left Ventricle Quantification Through Spatio-Temporal CNNs. 466-475 - Yeonggul Jang, Sekeun Kim, Hackjoon Shim, Hyuk-Jae Chang:
Full Quantification of Left Ventricle Using Deep Multitask Network with Combination of 2D and 3D Convolution on 2D + t Cine MRI. 476-483
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