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

TL;DR

This paper introduces SK-Unet, an enhanced U-net model with selective kernel modules that integrates multi-sequence cardiac MRI data for improved segmentation of cardiac structures, demonstrating high accuracy on MICCAI challenge data.

Contribution

The paper presents a novel U-net variant incorporating selective kernel and residual modules, leveraging multi-sequence MRI data to improve cardiac segmentation accuracy.

Findings

Achieved mean dice scores of 0.922 for LV, 0.827 for LVM, and 0.874 for RV.

Utilized multi-sequence MRI data to enhance segmentation performance.

Demonstrated superior results on MICCAI challenge dataset.

Abstract

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.