UMamba-Dual: A dual-branch model based on UMamba for cesarean scar disorder segmentation

Abstract

Introduction and Hypothesis:

Accurate segmentation of cesarean scar disorder (CSDi) in ultrasound images is crucial for clinical diagnosis, disease monitoring, and personalized treatment. However, the ambiguous boundaries and complex anatomical structures of CSDi pose significant challenges. To address this, we propose UMamba-Dual, a dual-branch model derived from UMamba, designed to enhance segmentation performance in CSDi regions and provide reliable imaging support for clinical decision-making.

Methods:

UMamba-Dual integrates the strengths of two enhanced branches: Dual-Bot, incorporating squeeze-and-excitation (SE) attention, and Dual-Enc, employing a feature pyramid network (FPN) for improved feature representation and multi-scale perception. The training dataset included 1200 augmented 2D ultrasound images from 300 originals via flipping and rotation. An independent test set of 32 images was randomly selected and excluded from training and validation. Model performance was evaluated using Dice similarity coefficient, Intersection over Union (IoU), and Normalized Surface Dice (NSD), and compared with classical segmentation models such as nnUNet and VM-UNet.

Results:

UMamba-Dual achieved superior performance with Dice = 0.832, IoU = 0.782, and NSD = 0.788, consistently outperforming both classical models (UNet, nnUNet) and the recent VM-UNet, as well as the internal baselines (UMamba_Bot, UMamba_Enc).

Conclusions:

UMamba-Dual enables more accurate and robust segmentation of CSDi regions in ultrasound images, particularly in cases characterized by ambiguous boundaries or irregular anatomical structures. These results highlight its potential for reliable clinical application.