Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI

[1]  Shuo Li,et al.  Multi-Indices Quantification for Left Ventricle via DenseNet and GRU-Based Encoder-Decoder with Attention , 2021, Complex..

[2]  Jens Petersen,et al.  nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation , 2020, Nature Methods.

[3]  Peter M. Full,et al.  Studying Robustness of Semantic Segmentation under Domain Shift in cardiac MRI , 2020, M&Ms and EMIDEC/STACOM@MICCAI.

[4]  F. Maes,et al.  Shape Constrained CNN for Cardiac MR Segmentation with Simultaneous Prediction of Shape and Pose Parameters , 2020, M&Ms and EMIDEC/STACOM@MICCAI.

[5]  Dorit Merhof,et al.  A Method for Semantic Knee Bone and Cartilage Segmentation with Deep 3D Shape Fitting Using Data from the Osteoarthritis Initiative , 2020, ShapeMI@MICCAI.

[6]  Yifan Chen,et al.  A 3D+2D CNN Approach Incorporating Boundary Loss for Stroke Lesion Segmentation , 2020, MLMI@MICCAI.

[7]  Huiyu Li,et al.  Deep Distance Map Regression Network with Shape-Aware Loss for Imbalanced Medical Image Segmentation , 2020, MLMI@MICCAI.

[8]  Bin Dong,et al.  Deep Active Contour Network for Medical Image Segmentation , 2020, MICCAI.

[9]  M. Pollefeys,et al.  Probabilistic 3D Surface Reconstruction from Sparse MRI Information , 2020, MICCAI.

[10]  Leon Axel,et al.  PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data , 2020, ArXiv.

[11]  Xiao Chen,et al.  Anatomy-Aware Cardiac Motion Estimation , 2020, MLMI@MICCAI.

[12]  Xiahai Zhuang,et al.  Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shape Attention , 2020, MICCAI.

[13]  Hervé Delingette,et al.  A Deep Learning based Fast Signed Distance Map Generation , 2020, ArXiv.

[14]  Yong Yin,et al.  Shape-Aware Organ Segmentation by Predicting Signed Distance Maps , 2019, AAAI.

[15]  Konstantinos Kamnitsas,et al.  Explainable Shape Analysis through Deep Hierarchical Generative Models: Application to Cardiac Remodeling , 2019, ArXiv.

[16]  Septimiu E. Salcudean,et al.  Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks , 2019, IEEE Transactions on Medical Imaging.

[17]  Yong Xia,et al.  Boundary-Aware Network for Kidney Tumor Segmentation , 2020, MLMI@MICCAI.

[18]  Anonymous Name,et al.  How Distance Transform Maps Boost Segmentation CNNs: An Empirical Study , 2020 .

[19]  Pablo Lamata,et al.  Left Ventricle Quantification with Cardiac MRI: Deep Learning Meets Statistical Models of Deformation , 2019, STACOM@MICCAI.

[20]  Frederik Maes,et al.  Left Ventricular Parameter Regression from Deep Feature Maps of a Jointly Trained Segmentation CNN , 2019, STACOM@MICCAI.

[21]  Bjoern H Menze,et al.  Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation , 2019, MLMI@MICCAI.

[22]  Nils Gessert,et al.  Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs , 2019, STACOM@MICCAI.

[23]  Daniel Rueckert,et al.  Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images , 2019, MICCAI.

[24]  Wenjia Bai,et al.  Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function , 2019, JACC. Cardiovascular imaging.

[25]  Olivier Bernard,et al.  Cardiac MRI Segmentation with Strong Anatomical Guarantees , 2019, MICCAI.

[26]  Alejandro F. Frangi,et al.  3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata , 2019, MICCAI.

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

[28]  Zhiming Luo,et al.  Convolutional Neural Network With Shape Prior Applied to Cardiac MRI Segmentation , 2019, IEEE Journal of Biomedical and Health Informatics.

[29]  Ben Glocker,et al.  TeTrIS: Template Transformer Networks for Image Segmentation With Shape Priors , 2019, IEEE Transactions on Medical Imaging.

[30]  Ziv Yaniv,et al.  A Distance Map Regularized CNN for Cardiac Cine MR Image Segmentation , 2019, Medical physics.

[31]  Jose Dolz,et al.  Boundary loss for highly unbalanced segmentation , 2018, MIDL.

[32]  Hervé Delingette,et al.  Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow , 2018, Medical Image Anal..

[33]  Max A. Viergever,et al.  A deep learning framework for unsupervised affine and deformable image registration , 2018, Medical Image Anal..

[34]  Daniel Rueckert,et al.  Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach , 2018, IEEE Transactions on Medical Imaging.

[35]  Ganapathy Krishnamurthi,et al.  Fully convolutional multi‐scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers , 2018, Medical Image Anal..

[36]  et al.,et al.  Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge , 2018, ArXiv.

[37]  Ross T. Whitaker,et al.  DeepSSM: A Deep Learning Framework for Statistical Shape Modeling from Raw Images , 2018, ShapeMI@MICCAI.

[38]  Ziv Yaniv,et al.  Left Ventricle Segmentation and Quantification from Cardiac Cine MR Images via Multi-task Learning , 2018, STACOM@MICCAI.

[39]  Hao Xu,et al.  Calculation of Anatomical and Functional Metrics Using Deep Learning in Cardiac MRI: Comparison Between Direct and Segmentation-Based Estimation , 2018, STACOM@MICCAI.

[40]  Qian Tao,et al.  ESU-P-Net: Cascading Network for Full Quantification of Left Ventricle from Cine MRI , 2018, STACOM@MICCAI.

[41]  J. Alison Noble,et al.  &OHgr;‐Net (Omega‐Net): Fully automatic, multi‐view cardiac MR detection, orientation, and segmentation with deep neural networks☆ , 2018, Medical Image Anal..

[42]  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.

[43]  Konstantinos Kamnitsas,et al.  Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation , 2017, IEEE Transactions on Medical Imaging.

[44]  Roberto Cipolla,et al.  Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[45]  Wufeng Xue,et al.  Full left ventricle quantification via deep multitask relationships learning , 2018, Medical Image Anal..

[46]  Jun Zhang,et al.  Multi-task neural networks for joint hippocampus segmentation and clinical score regression , 2018, Multimedia Tools and Applications.

[47]  Örjan Smedby,et al.  Automatic Whole Heart Segmentation Using Deep Learning and Shape Context , 2017, STACOM@MICCAI.

[48]  Fausto Milletari,et al.  Integrating Statistical Prior Knowledge into Convolutional Neural Networks , 2017, MICCAI.

[49]  Klaus H. Maier-Hein,et al.  Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges , 2017, Lecture Notes in Computer Science.

[50]  Wufeng Xue,et al.  Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression Learning , 2017, IEEE Transactions on Medical Imaging.

[51]  Xiahai Zhuang,et al.  Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges , 2017, Lecture Notes in Computer Science.

[52]  Ole Winther,et al.  Ladder Variational Autoencoders , 2016, NIPS.

[53]  Andrew Zisserman,et al.  Spatial Transformer Networks , 2015, NIPS.

[54]  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.

[55]  S. Dymarkowski,et al.  Clinical cardiac MRI , 2005 .

[56]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[57]  Jan Flusser,et al.  On the independence of rotation moment invariants , 2000, Pattern Recognit..

[58]  Olivier D. Faugeras,et al.  Statistical shape influence in geodesic active contours , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[59]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..