MSFSegNet: A multi-scale feature fusion model for instance segmentation in adult liver ultrasound images

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.