Abstract: Because of its advantages of no ionizing radiation, Cardiac Cine Magnetic Resonance  Imag- ing (CMRI) has become the main method in medical diagnosis, and the accurate classification and re- cognition of left ventricular, right ventricular and left myocardium is an important step before the heart surgery. However, manual segmentation of cardiac structure time-consuming and error-prone, so automatic segmentation of both ventricular and myocardium is crucial. Firstly, U-Net++ is selected as the basic network framework. Secondly, in order to improve the feature reuse rate and solve the overfitting issue caused by the increase of network depth, a dense residual module is proposed in the encoding part of U-Net++, so that more features can be learned during the network down-sampling process. In addition, in the decoding part, in order to make the network segmentation results more in line with the target organ between the physical characteristics, multiple convolution kernels are used to expand the receptive field and a long distance dependent module is used share the global context information to make the network in the decoding process as much as possible to get the relationship information between the target organs and make the segmentation result more accurate. Finally, considering the consistency and uniqueness between the biventricle and the left myocardium, the post-treatment operation of obtaining the maximum connectivity domain and filling the small holes is added. The experimental data used in this paper is the ACDC Cardiac Segmentation Challenge data set, which includes SHORT axial MRI images of 150 volunteers at the end of systolic and diastolic phases. In this paper, the test set of this data set is verified, and the experimental results are obtained by online submission. Experimental results show that, compared with other methods, the proposed method can effectively segment the target organ. Specially, the Dice coefficient at the end of diastolic period reaches 0.96, 0.94 and 0.89 respectively in the left ventricle, right ventricle and left myocardium, and the segmentation precision at the end of systolic period reaches 0.87, 0.86 and 0.89 respectively.

Key words: image processing, medical image；bi-ventricles and myocardium, magnetic resonance image, deep learning