DMRNet: a dynamic multi-scale residual network for Shamrock view and lumbar plexus segmentation

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Mean Intersection over Union: 0.863, Mean Dice coefficient: 0.926

• Primary Outcome: Segmentation accuracy of lumbar plexus structures in Shamrock view ultrasound images

• Nnt Or Sensitivity: Sensitivity and specificity not reported - only segmentation overlap metrics provided

• Confidence Interval: Not reported

Clinical Application

Requires integration with existing ultrasound systems and adequate computational resources - practical implementation details not provided Relevant to Australian anesthesia practice where ultrasound-guided regional blocks are standard care. Would need TGA approval for clinical use and integration with existing ultrasound equipment used in Australian hospitals Anesthesiologists performing ultrasound-guided lumbar plexus blocks for hip and knee surgeries

Abstract

Lumbar plexus block (LPB) is a regional anesthesia technique widely used for hip and knee surgeries. However, despite the assistance of ultrasound guidance, the complex anatomical structure of the lumbar plexus poses significant challenges for anesthesiologists during the procedure. To accurately identify the lumbar plexus located in the posterior third of the psoas major in the Shamrock view, a deep learning-based segmentation model named DMRNet was proposed. This model is designed to precisely delineate muscles, nerves, and bony structures in Shamrock view ultrasound images. DMRNet integrates several innovative modules, including Adaptive Multi-Scale Dilated (AMD) Module that enhances the model's ability to capture multi-scale features; Dense Attention Residual (DAR) Module that adaptively selects salient feature regions; Attention-Enhanced Hybrid (AEH) Module that emphasizes critical features while suppressing irrelevant ones; and two attention mechanisms, Boundary-Aware Spatial Attention Mechanism (BASA) and the Enhanced Residual Multi-Head Attention Mechanism (ER-MHA), that improve the model's capacity to recognize complex contextual patterns. Experimental results demonstrated that DMRNet achieved a mean Intersection over Union of 0.863 and a mean Dice coefficient of 0.926 across all target structures, outperforming other state-of-the-art models. These findings suggest that DMRNet may serve as a useful assistive tool for sonoanatomical recognition in Shamrock view ultrasound images and may provide educational support for ultrasound-guided LPB training. A dynamic multi-scale residual network is proposed for lumbar lexus (LP) segmentation.Two attention mechanisms were improved, and three modules were proposed to improve downsampling, skip connection, and upsampling.We built our own Shamrock view ultrasound database containing the lumbar plexus nerves, and the Anesthesiologist physician of the anesthesiology department annotated it using Labelme.External validation was performed using the Ultrasonic Brachial Plexus Dataset (UBPD).

References

1. 1.Cui, H., Wang, Y., Lin, L., Wu, Q., Zhou, M., Guo, W., Shen, Y., & Zou, Z. (2026). DMRNet: a dynamic multi-scale residual network for Shamrock view and lumbar plexus segmentation. Computer Assisted Surgery, Article 2677269. https://doi.org/10.1080/24699322.2026.2677269