DeepCQ: Deep multi-task conditional quantification network for estimation of left ventricle parameters. (February 2020) - Chen, Ruifeng; Xu, Chenchu; Dong, Zhangfu; Liu, Yueguo; Du, Xiuquan
Highlights: Multi-task learning framework for LV parameters estimation and myocardium segmentation. Using LV structural information for alleviating the gradient disappearance issue. Uncertainty to weigh loss for preforming multiple tasks automatically. Abstract: Background and objective: Automatic cardiac left ventricle (LV) quantification plays an important role in assessing cardiac function. Although many advanced methods have been put forward to quantify related LV parameters, automatic cardiac LV quantification is still a challenge task due to the anatomy construction complexity of heart. Methods: In this work, we propose a novel deep multi-task conditional quantification learning model (DeepCQ) which contains Segmentation module, Quantification encoder, and Dynamic analysis module. Besides, we also use task uncertainty loss function to update the parameters of the network in training. Results: The proposed framework is validated on the dataset from Left Ventricle Full Quantification Challenge MICCAI 2018 (https://lvquan18.github.io/ ). The experimental results show that DeepCQ outperforms the other advanced methods. Conclusions: It illustrates that our method has a great potential in comprehensive cardiac function assessment and could play an auxiliary role in clinicians' diagnosis.