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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 addition, 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 muscle, which can assist doctors to make diagnosis. [ABSTRACT FROM AUTHOR]