An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation
- 15 March 2018
- book chapter
- conference paper
- Published by Springer Science and Business Media LLC
Abstract
No abstract availableKeywords
Other Versions
This publication has 12 references indexed in Scilit:
- A survey on deep learning in medical image analysisMedical Image Analysis, 2017
- 3D U-Net: Learning Dense Volumetric Segmentation from Sparse AnnotationPublished by Springer Science and Business Media LLC ,2016
- V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image SegmentationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Stratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac ImagesIEEE Transactions on Medical Imaging, 2016
- A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRIMedical Image Analysis, 2016
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet ClassificationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Fully convolutional networks for semantic segmentationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Multi-atlas segmentation with augmented features for cardiac MR imagesMedical Image Analysis, 2015
- Cardiovascular disease in Europe 2014: epidemiological updateEuropean Heart Journal, 2014
- A Probabilistic Patch-Based Label Fusion Model for Multi-Atlas Segmentation With Registration Refinement: Application to Cardiac MR ImagesIEEE Transactions on Medical Imaging, 2013