论文标题
SK-UNET：具有选择性内核的改进的U-NET模型，用于分割多序列心脏MR
SK-Unet: an Improved U-net Model with Selective Kernel for the Segmentation of Multi-sequence Cardiac MR
论文作者
论文摘要
在临床环境中，主要基于晚期的Gadolinium增强（LGE）心脏磁共振图像（CMRIS）评估心肌梗死（MI）作为一种COM-MON心血管疾病。 LGE CMRI中的左心室（LV），右心室（RV）和左VEN-TRICULAR心肌（LVM）的自动示意分割是诊所中的辅助诊断。为了完成此细分任务，本文通过结合包括Cine，LGE和T2加权CMRI的多序列CMRI来提出修改后的U-NET体系结构。 CINE和T2加权CMRI用于协助LGE CMRIS中的分割。在此分割网络中，将挤压和激发残差（SE-RES）和选择性内核（SK）模块插入到下采样和上采样阶段中，分别相应。 SK模块使获得的特征地图在空间和频道空间中都更有信息，并获得了更精确的分割结果。使用的数据集来自MICCAI挑战（MS-CMRSEG 2019），该数据集是从45名患者中获得的，其中包括三个CMR序列。从35例患者中获得的CINE和T2加权CMRI，并从5例患者中获得的LGE CMRI被标记。我们的方法在LGE CMRIS中达到了平均骰子得分为0.922（LV），0.827（LVM）和0.874（RV）。
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.
