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<p>Uterine fibroids represent one of the most prevalent gynecological tumors; however, their ultrasound images frequently exhibit indistinct boundaries and complex morphologies, thereby complicating accurate segmentation. An enhanced Swin-Unet-based framework, designated the Swin-Unet Edge-Sensitive Segmentation (SES) network, is proposed herein to advance boundary delineation and segmentation accuracy. The SES network incorporates the Residual Channel Attention Network (RCAN) to recalibrate feature responses via channel attention weighting, thereby reinforcing the representation of lesion regions, and the Richer Convolutional Features (RCF) module to preserve multi-scale spatial information through hierarchical feature integration, effectively addressing pixel-level classification in regions with blurred boundaries. The model was evaluated on annotated ultrasound images provided by Shanxi Provincial Children’s Hospital. Experimental findings demonstrate that SES consistently outperforms established architectures, including U-Net, U-Net++, Attention U-Net, and TransUNet, achieving superior performance across multiple indices (Dice coefficient: 0.9452; IoU: 0.8721; accuracy: 0.9358). Ablation analyses further substantiate the pivotal contributions of the RCAN and RCF modules to the overall segmentation performance. The proposed SES framework integrates global modeling capacity, multi-scale attention mechanisms, and edge-sensitive feature extraction to deliver a more accurate and robust solution for the ultrasound image segmentation of uterine fibroids, highlighting its substantial potential for clinical application.</p>

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Ses: a Swin-Unet Edge-aware Segmentation network for uterine fibroid ultrasound images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月4日

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  • Xiaotong Wang,
  • Liling Shi,
  • Wenjuan Wang,
  • Lijuan Guo

摘要

Uterine fibroids represent one of the most prevalent gynecological tumors; however, their ultrasound images frequently exhibit indistinct boundaries and complex morphologies, thereby complicating accurate segmentation. An enhanced Swin-Unet-based framework, designated the Swin-Unet Edge-Sensitive Segmentation (SES) network, is proposed herein to advance boundary delineation and segmentation accuracy. The SES network incorporates the Residual Channel Attention Network (RCAN) to recalibrate feature responses via channel attention weighting, thereby reinforcing the representation of lesion regions, and the Richer Convolutional Features (RCF) module to preserve multi-scale spatial information through hierarchical feature integration, effectively addressing pixel-level classification in regions with blurred boundaries. The model was evaluated on annotated ultrasound images provided by Shanxi Provincial Children’s Hospital. Experimental findings demonstrate that SES consistently outperforms established architectures, including U-Net, U-Net++, Attention U-Net, and TransUNet, achieving superior performance across multiple indices (Dice coefficient: 0.9452; IoU: 0.8721; accuracy: 0.9358). Ablation analyses further substantiate the pivotal contributions of the RCAN and RCF modules to the overall segmentation performance. The proposed SES framework integrates global modeling capacity, multi-scale attention mechanisms, and edge-sensitive feature extraction to deliver a more accurate and robust solution for the ultrasound image segmentation of uterine fibroids, highlighting its substantial potential for clinical application.