Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge

[1]  Xiahai Zhuang,et al.  Unsupervised Domain Adaptation With Variational Approximation for Cardiac Segmentation , 2021, IEEE Transactions on Medical Imaging.

[2]  Xiahai Zhuang,et al.  CF Distance: A New Domain Discrepancy Metric and Application to Explicit Domain Adaptation for Cross-Modality Cardiac Image Segmentation , 2020, IEEE Transactions on Medical Imaging.

[3]  Xiaowei Ding,et al.  Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation , 2019, Medical Image Anal..

[4]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Maxime Sermesant,et al.  Style Data Augmentation for Robust Segmentation of Multi-modality Cardiac MRI , 2019, STACOM@MICCAI.

[6]  Ling He,et al.  SK-Unet: An Improved U-Net Model with Selective Kernel for the Segmentation of Multi-sequence Cardiac MR , 2019, STACOM@MICCAI.

[7]  Jiexiang Wang,et al.  Multi-sequence Cardiac MR Segmentation with Adversarial Domain Adaptation Network , 2019, STACOM@MICCAI.

[8]  Daguang Xu,et al.  Cardiac Segmentation of LGE MRI with Noisy Labels , 2019, STACOM@MICCAI.

[9]  Gongning Luo,et al.  An Automatic Cardiac Segmentation Framework based on Multi-sequence MR Image , 2019, STACOM@MICCAI.

[10]  Víctor M. Campello,et al.  Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI , 2019, STACOM@MICCAI.

[11]  Hongwei Li,et al.  Adversarial Convolutional Networks with Weak Domain-Transfer for Multi-Sequence Cardiac MR Images Segmentation , 2019, STACOM@MICCAI.

[12]  Nishant Ravikumar,et al.  Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation , 2019, STACOM@MICCAI.

[13]  D. Rueckert,et al.  Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation , 2019, STACOM@MICCAI.

[14]  Xiahai Zhuang,et al.  Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors , 2019, MICCAI.

[15]  Jian Yang,et al.  Selective Kernel Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Guang Yang,et al.  Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge , 2019, Medical Image Anal..

[17]  Shunxing Bao,et al.  SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth , 2018, IEEE Transactions on Medical Imaging.

[18]  Xiahai Zhuang,et al.  Multivariate Mixture Model for Myocardial Segmentation Combining Multi-Source Images , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Lixu Gu,et al.  Multi-sequence myocardium segmentation with cross-constrained shape and neural network-based initialization , 2019, Comput. Medical Imaging Graph..

[20]  Richard James Housden,et al.  Algorithms for left atrial wall segmentation and thickness – Evaluation on an open-source CT and MRI image database , 2018, Medical Image Anal..

[21]  Hervé Delingette,et al.  Automatically Segmenting the Left Atrium from Cardiac Images Using Successive 3D U-Nets and a Contour Loss , 2018, STACOM@MICCAI.

[22]  Andreas K. Maier,et al.  Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI , 2018, STACOM@MICCAI.

[23]  Jie Liu,et al.  Myocardium segmentation from DE MRI with guided random walks and sparse shape representation , 2018, International Journal of Computer Assisted Radiology and Surgery.

[24]  Xin Yang,et al.  Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? , 2018, IEEE Transactions on Medical Imaging.

[25]  Jan Kautz,et al.  Multimodal Unsupervised Image-to-Image Translation , 2018, ECCV.

[26]  Harshad Rai,et al.  Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks , 2018 .

[27]  Aaron Carass,et al.  Whole Brain Segmentation and Labeling from CT Using Synthetic MR Images , 2017, MLMI@MICCAI.

[28]  Lixu Gu,et al.  Myocardium Segmentation From DE MRI Using Multicomponent Gaussian Mixture Model and Coupled Level Set , 2017, IEEE Transactions on Biomedical Engineering.

[29]  Konstantinos Kamnitsas,et al.  Unsupervised domain adaptation in brain lesion segmentation with adversarial networks , 2016, IPMI.

[30]  Xiahai Zhuang,et al.  Multivariate Mixture Model for Cardiac Segmentation from Multi-Sequence MRI , 2016, MICCAI.

[31]  Alejandro F. Frangi,et al.  Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images , 2016, Medical Image Anal..

[32]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Michael Jerosch-Herold,et al.  T1 Mapping: Basic Techniques and Clinical Applications. , 2016, JACC. Cardiovascular imaging.

[34]  Rob J van der Geest,et al.  Automated left ventricle segmentation in late gadolinium‐enhanced MRI for objective myocardial scar assessment , 2015, Journal of magnetic resonance imaging : JMRI.

[35]  Jürgen Weese,et al.  Benchmark for Algorithms Segmenting the Left Atrium From 3D CT and MRI Datasets , 2015, IEEE Transactions on Medical Imaging.

[36]  Daniel Rueckert,et al.  Right ventricle segmentation from cardiac MRI: A collation study , 2015, Medical Image Anal..

[37]  Jie Liu,et al.  Myocardium segmentation combining T2 and DE MRI using Multi-Component Bivariate Gaussian mixture model , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[38]  Daniel J. Perry,et al.  Evaluation of current algorithms for segmentation of scar tissue from late Gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge , 2013, Journal of Cardiovascular Magnetic Resonance.

[39]  Xiahai Zhuang,et al.  Challenges and methodologies of fully automatic whole heart segmentation: a review. , 2013, Journal of healthcare engineering.

[40]  Dong Wei,et al.  Three-dimensional segmentation of the left ventricle in late gadolinium enhanced MR images of chronic infarction combining long- and short-axis information , 2013, Medical Image Anal..

[41]  Perry E Radau,et al.  Automatic myocardium segmentation of LGE MRI by deformable models with prior shape data , 2013, Journal of Cardiovascular Magnetic Resonance.

[42]  Alain Lalande,et al.  Automatic Quantification of Myocardial Infarction from Delayed Enhancement MRI , 2011, 2011 Seventh International Conference on Signal Image Technology & Internet-Based Systems.

[43]  Simon K. Warfield,et al.  Left Ventricular Segmentation Challenge from Cardiac MRI: A Collation Study , 2011, STACOM.

[44]  Dong Wei,et al.  Myocardial Segmentation of Late Gadolinium Enhanced MR Images by Propagation of Contours from Cine MR Images , 2011, MICCAI.

[45]  Vivek Muthurangu,et al.  Evaluation of techniques for the quantification of myocardial scar of differing etiology using cardiac magnetic resonance. , 2011, JACC. Cardiovascular imaging.

[46]  M. Lythgoe,et al.  Comparison of segmentation methods for MRI measurement of cardiac function in rats , 2010, Journal of magnetic resonance imaging : JMRI.

[47]  G. Wright,et al.  Evaluation Framework for Algorithms Segmenting Short Axis Cardiac MRI. , 2009, The MIDAS Journal.

[48]  Daniel Rueckert,et al.  Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II , 2017, Lecture Notes in Computer Science.

[49]  Marcus Carlsson,et al.  Magnetic resonance imaging as a potential gold standard for infarct quantification. , 2008, Journal of electrocardiology.

[50]  Fred S Apple,et al.  Universal definition of myocardial infarction. , 2007, Journal of the American College of Cardiology.

[51]  Torsten Rohlfing,et al.  Shape-Based Averaging , 2007, IEEE Transactions on Image Processing.

[52]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[53]  Thomas O'Donnell,et al.  Quantification of Delayed Enhancement MR Images , 2004, MICCAI.

[54]  Dan W Rettmann,et al.  Accurate and Objective Infarct Sizing by Contrast-enhanced Magnetic Resonance Imaging in a Canine Myocardial Infarction Model , 2022 .

[55]  R. Kim,et al.  Contrast-enhanced MRI and routine single photon emission computed tomography (SPECT) perfusion imaging for detection of subendocardial myocardial infarcts: an imaging study , 2003, The Lancet.