Segmentation of ventricles from cardiac magnetic resonance (MR) images is a key step to obtaining clinical parameters useful for prognosis of cardiac pathologies. To improve upon the performance of existing fully convolutional network (FCN) based automatic right ventricle (RV) segmentation approaches, a multi-task deep neural network (DNN) architecture is proposed. The multi-task model can employ any FCN as a building block, allows for leveraging shared features between different tasks, and can be efficiently trained end-to-end. Specifically, a multi-task U-net is developed and implemented using the Tensorflow framework. Numerical tests on real datasets showcase the merits of the proposed approach and in particular its ability to offer improved segmentation performance for small-size RVs.
|Original language||English (US)|
|Title of host publication||2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - Sep 10 2018|
|Event||2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada|
Duration: Apr 15 2018 → Apr 20 2018
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Other||2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018|
|Period||4/15/18 → 4/20/18|
Bibliographical noteFunding Information:
This work was supported by NIH R00HL111410 and NSF grants 1509040, 1711471, 1500713, and 1651825.
- Convolutional neural networks
- Multi-task learning
- Right ventricle segmentation