SK-Unet: An Improved U-Net Model With Selective Kernel for the Segmentation of LGE Cardiac MR Images

In the clinical environment, myocardial infarction (MI) as one common cardiovascular disease is mainly evaluated using late gadolinium enhancement (LGE) cardiac magnetic resonance images (CMRIs). Accurate segmentation of the ventricles and the myocardium is a prerequisite for quantitative assessment of cardiac functions and disease progression. Performing the task using LGE images is, however, rather challenging due to heterogeneous image intensity distribution and lack of clear boundaries between adjacent organs and tissues. In this paper we propose a deep neural network method for automatic segmentation of the left ventricle (LV), right ventricle (RV), and left ventricular myocardium (LVM) from LGE CMRIs, which also leverages complementary information from cine and T2-weighted CMRIs if available. In the proposed method, termed SK-Unet, we augment the original U-Net model by adding a squeeze-and-excitation residual (SE-Res) module in the encoder and a selective kernel (SK) module in the decoder. The SE-Res module applies an attention mechanism to enhance informative feature extraction and suppress redundant ones. The SK module offers the ability to adaptively learn task-relevant multi-scale spatial features. We tested our method by participating in the MICCAI 2019 MS-CMRSeg challenge and achieved a mean dice score of 0.922 for LV segmentation, 0.827 for LVM, and 0.874 for RV. The results placed our method at the 1st place in the competition, and our accuracy of 0.827 also greatly surpasses the measured inter-observer agreement of 0.757 for manual segmentation of LVM in LGE CMRIs. The code accompanying our method is made available online at
https://github.com/Xiyue-Wang/1st-in-MS-CMRSeg-2019
.