Zhang Liang, Karanikolas Georgios Vasileios, Akçakaya Mehmet, Giannakis Georgios B
Digital Tech. Center and Dept. of ECE, Univ. of Minnesota, Minneapolis, MN 55455, USA.
Proc IEEE Int Conf Acoust Speech Signal Process. 2018 Apr;2018:6677-6681. doi: 10.1109/ICASSP.2018.8461556. Epub 2018 Sep 13.
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.
从心脏磁共振（MR）图像中分割心室是获取对心脏疾病预后有用的临床参数的关键步骤。为了改进现有的基于全卷积网络（FCN）的自动右心室（RV）分割方法的性能，提出了一种多任务深度神经网络（DNN）架构。该多任务模型可以采用任何FCN作为构建模块，允许利用不同任务之间的共享特征，并且可以进行高效的端到端训练。具体而言，使用Tensorflow框架开发并实现了一个多任务U-net。在真实数据集上的数值测试展示了所提出方法的优点，特别是其为小尺寸右心室提供改进分割性能的能力。
