基于改进U2-Net网络的多裂肌MRI图像分割算法

Segmentation of multifidus muscle MRI images via improved U2-Net

针对腰间盘突出患者MRI图像多裂肌病变部位分割精度较低的问题,提出一种改进的U2-Net网络的新模型,目标是使得编码和解码的子网络通过一系列嵌套的跳跃路径来相互连接.重新设计U2-Net模型中RSU-7、RSU-6、RSU-5、RSU-4中间的跳跃连接,RSU-4F部分不变,用来降低编码解码子网络中特征图的语义缺失.为了提取到高质量的多裂肌特征,加入通道注意力模块,通过学习每个通道的权重,使网络能够更好地关注对任务有贡献的通道,从而提升模型的性能.为验证模型的有效性,在多裂肌MRI图像数据集上进行实验,发现相较于U-Net、U2-Net、U-Net++网络结构,骰子系数(Dice)、豪斯多夫距离(HD)以及均交并比(MIoU)3个指标均有优化.实验结果表明,本文提出的算法对于多裂肌的MRI图像分割有较好的效果,能够辅助医生对病情做出判断.

To address the low segmentation accuracy of multifidus muscle lesion sites in MRI images of patients with lumbar disc herniation,this paper proposes a new model to improve the U2-Net network with the goal that the encoding and decoding subnetworks are interconnected by a series of nested jump paths.To reduce the semantic missing of feature maps in the encoding and decoding subnetworks,the jump connections in the middle of RSU-7,RSU-6,RSU-5,and RSU-4 in the U2-Net model are redesigned,while the RSU-4F part remains unchanged.In addi-tion,the channel attention module is added to enable the net to focus on channels of higher contribution to task,thus extract high quality multifractal muscle features.The experiments on the multifidus muscle MRI image dataset show that the improved U2-Net outperforms U-Net,U2-Net and U-Net++network in indicators of Dice,HD and MIoU.It can be concluded that the proposed approach has good performance on MRI image segmentation of multifidus mus-cle,which can assist doctors to make diagnosis.