  In the clinical environment, myocardial infarction (MI) as one com-mon
cardiovascular disease is mainly evaluated based on the late gadolinium
enhancement (LGE) cardiac magnetic resonance images (CMRIs). The auto-matic
segmentations of left ventricle (LV), right ventricle (RV), and left
ven-tricular myocardium (LVM) in the LGE CMRIs are desired for the aided
diag-nosis in clinic. To accomplish this segmentation task, this paper proposes
a modified U-net architecture by combining multi-sequence CMRIs, including the
cine, LGE, and T2-weighted CMRIs. The cine and T2-weighted CMRIs are used to
assist the segmentation in the LGE CMRIs. In this segmentation net-work, the
squeeze-and-excitation residual (SE-Res) and selective kernel (SK) modules are
inserted in the down-sampling and up-sampling stages, respective-ly. The SK
module makes the obtained feature maps more informative in both spatial and
channel-wise space, and attains more precise segmentation result. The utilized
dataset is from the MICCAI challenge (MS-CMRSeg 2019), which is acquired from
45 patients including three CMR sequences. The cine and T2-weighted CMRIs
acquired from 35 patients and the LGE CMRIs acquired from 5 patients are
labeled. Our method achieves the mean dice score of 0.922 (LV), 0.827 (LVM),
and 0.874 (RV) in the LGE CMRIs.
