Objective: Preoperative evaluation of vessels and nerves will help surgeons to understand and design the intermuscular approach individually. The aim of this study is to analyze the ability of computed tomography (CT) and magnetic resonance imaging (MRI) to display the vessels and nerves for paraspinal intermuscular approach to lumbar spine. Methods: A total of 18 healthy volunteers were examined by CT angiography (CTA) and MRI (sagittal, transverse T2WI and coronary nerve imaging) respectively. The vessels and nerve surrounding the paraspinal intermuscular space (Wiltse, Watkins and Weaver space) was observed. Results: In CTA, the blood supply of lumbar spine were originated from the lumbar artery. All the lumbar artery coursed between the upper and the lower pedicle of vertebral arch, and no vessels were found in the lateral sides of pedicle. In the lateral border of L1–S1, lumbar arteries sent branches into Wiltse space. All levels showed no obvious vessels into Weaver space. Slightly below the transverse process at L1–L3 levels, the lumbar arteries sent a branch into the Watkins space, and finally into the erector spinae. In the MR sagittal T2WI phase, lumbar artery were visible by the blank empty signal. The course of artery was similar with CT. Coronal nerve imaging can clearly show the ventral root and dorsal root, spinal ganglia, and spinal nerve. The relationship between spinal nerve and intermuscular space was failed to show. In the transverse T2WI, the vessel were visible in the Wiltse and Watkins space which similar with CT. Conclusion: Preoperative imaging analysis of vessels and nerves surrounding paraspinal muscle is beneficial to the design of paraspinal intermuscular approach to lumbar spine.
Background For the coding part of U-Net3+, the brain tumor feature extraction ability is insufficient, leading to insufficient feature fusion when sampling on the network and reducing the segmentation accuracy. Methods In this study, we propose an improved U-Net3+ segmentation network based on stage residual. In the encoder part, the encoder based on the stage residual structure is used to reduce the degradation problem caused by the increase in network depth and enhance the feature extraction ability of the encoder, which is convenient for full feature fusion when sampling on the network. Besides, we used a filter response normalization (FRN) layer instead of a batch normalization layer to eliminate batch size impact on the network. Based on the improved U-Net3+ two-dimensional (2D) model with stage residual, IResUnet3+ three-dimensional (3D) model is constructed. We explore appropriate methods to deal with 3D data, which achieve accurate segmentation of the 3D network. Results The experimental results showed that: the sensitivity of WT, TC, and ET increased by 1.34%, 4.6%, and 8.44%, respectively. And the Dice coefficients of ET and WT were further increased by 3.43% and 1.03%, respectively. To facilitate further research, source code can be found at: https://github.com/YuOnlyLookOne/IResUnet3Plus. Conclusion In the segmentation task of brain tumor brats2018 dataset, compared with the classical networks u-net, v-net, resunet and u-net3 +, the proposed network has smaller parameters and significantly improved accuracy.
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