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In the clinical environment, myocardial infarction (MI) as one com-moncardiovascular disease is mainly evaluated based on the late gadoliniumenhancement (LGE) cardiac magnetic resonance images (CMRIs). The auto-maticsegmentations of left ventricle (LV), right ventricle (RV), and leftven-tricular myocardium (LVM) in the LGE CMRIs are desired for the aideddiag-nosis in clinic. To accomplish this segmentation task, this paper proposesa modified U-net architecture by combining multi-sequence CMRIs, including thecine, LGE, and T2-weighted CMRIs. The cine and T2-weighted CMRIs are used toassist the segmentation in the LGE CMRIs. In this segmentation net-work, thesqueeze-and-excitation residual (SE-Res) and selective kernel (SK) modules areinserted in the down-sampling and up-sampling stages, respective-ly. The SKmodule makes the obtained feature maps more informative in both spatial andchannel-wise space, and attains more precise segmentation result. The utilizeddataset is from the MICCAI challenge (MS-CMRSeg 2019), which is acquired from45 patients including three CMR sequences. The cine and T2-weighted CMRIsacquired from 35 patients and the LGE CMRIs acquired from 5 patients arelabeled. Our method achieves the mean dice score of 0.922 (LV), 0.827 (LVM),and 0.874 (RV) in the LGE CMRIs.

SK-Unet: an Improved U-net Model with Selective Kernel for the Segmentation of Multi-sequence Cardiac MR