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Updated: May 20, 2025
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
Published on: July 5, 2024
MSFSegNet: A multi-scale feature fusion model for instance segmentation in adult liver ultrasound images
Xiuming Wu1, Weifeng Yu1, Lei Zhang2
1Department of Ultrasound, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, 362000, PR China.
Background And Objective:
Liver diseases often remain undetected until advanced stages due to the lack of early symptoms. Two-dimensional ultrasonography is a key diagnostic tool, but manual segmentation of liver and its accessory structures (LAS) is time-consuming and prone to human error. To address this, we propose MSFSegNet, a novel instance segmentation model designed for adult liver ultrasound images.
Methods:
MSFSegNet integrates a multi-scale feature fusion network (CCMC), an adaptive downsampling method (ODConv), and the Convolutional Block Attention Module (CBAM) to enhance segmentation accuracy, particularly for small anatomical structures.
Results:
MSFSegNet achieves superior performance with Precision, Recall, and mAP@0.5 of 94.4 %, 91.8 %, and 95.7 % in position evaluation, and 93.9 %, 91.3 %, and 94.8 % in segmentation tasks, outperforming existing methods by a significant margin.
Conclusions:
The proposed model demonstrates significant potential for computer-aided diagnosis in liver ultrasound imaging, offering a robust solution for accurate segmentation of LAS. Future work will focus on optimizing computational efficiency and expanding the model's applicability to pathological cases.
