| Title: | 3-D Consistent and Robust Segmentation of Cardiac Images by Deep Learning With Spatial Propagation |
| Journal: | IEEE Transactions on Medical Imaging |
| Published: | 29 Mar 2018 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/29994087/ |
| DOI: | https://doi.org/10.1109/tmi.2018.2820742 |
Publication 2159
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
We propose a method based on deep learning to perform cardiac segmentation on short axis Magnetic resonance imaging stacks iteratively from the top slice (around the base) to the bottom slice (around the apex). At each iteration, a novel variant of the U-net is applied to propagate the segmentation of a slice to the adjacent slice below it. In other words, the prediction of a segmentation of a slice is dependent upon the already existing segmentation of an adjacent slice. The 3-D consistency is hence explicitly enforced. The method is trained on a large database of 3078 cases from the U.K. Biobank. It is then tested on the 756 different cases from the U.K. Biobank and three other state-of-the-art cohorts (ACDC with 100 cases, Sunnybrook with 30 cases, and RVSC with 16 cases). Results comparable or even better than the state of the art in terms of distance measures are achieved. They also emphasize the assets of our method, namely, enhanced spatial consistency (currently neither considered nor achieved by the state of the art), and the generalization ability to unseen cases even from other databases.4 Authors
- Qiao Zheng
- Herve Delingette
- Nicolas Duchateau
- Nicholas Ayache
1 Application
| Application ID | Title |
|---|---|
| 2964 | Description of cardiovascular phenotype in the UK Biobank population based on cardiovascular magnetic resonance and carotid ultrasound |
Enabling scientific discoveries that improve human health