arXiv 2604.137912026-04-15cs.CV

PBE-UNet：一种轻量级的渐进边界增强U-Net，具有尺度感知聚合，用于超声图像分割

PBE-UNet: A light weight Progressive Boundary-Enhanced U-Net with Scale-Aware Aggregation for Ultrasound Image Segmentation

Department of Computer Science, Shaoxing University（绍兴大学计算机科学系）；National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University（工业智能化与系统优化国家级前沿科学中心，东北大学）；College of Computer Science, Chongqing University（重庆大学计算机学院）；School of Computer and Computing Science, Hangzhou City University（杭州市大学计算机与计算科学学院）；ZJU-UIUC Institute, Zhejiang University（浙江大学ZJU-UIUC研究院）；Information Technology R&D Innovation Center of Peking University（北京大学信息技术研发创新中心）

Chen Wang, Yixin Zhu, Yongbin Zhu, Fengyuan Shi, Qi Li, Jun Wang, Zuozhu Liu, Keli Hu

备注：: 14 pages, 14 figures

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AI总结：

本文提出PBE-UNet，通过引入尺度感知聚合模块和边界引导特征增强模块，解决超声图像分割中边界模糊和尺度变化的问题，实验表明其在四个数据集上优于现有方法。

摘要：

准确的超声图像病变分割对预防筛查和临床诊断至关重要，但受到低对比度、模糊边界和显著尺度变化的挑战。尽管现有深度学习方法表现优异，但仍难以处理尺度变化和不清晰的肿瘤边界。为此，我们提出渐进边界增强U-Net（PBE-UNet）。特别地，我们首先引入了一个尺度感知聚合模块（SAAM），动态调整其感受野以捕捉鲁棒的多尺度上下文信息。然后，我们提出边界引导特征增强（BGFE）模块以增强特征表示。我们发现狭窄边界与宽泛分割误差区域之间存在较大差距。与现有方法将边界视为静态掩码不同，BGFE模块逐步扩展狭窄边界预测，生成更宽的时空注意力图。因此，更宽的时空注意力图可以有效覆盖更宽的分割误差区域，并增强模型对这些挑战区域的关注。我们在四个基准超声数据集（BUSI、Dataset B、TN3K和BP）上进行了昂贵的实验。实验结果表明，所提出的PBE-UNet在四个数据集上优于现有方法。代码可在https://github.com/cruelMouth/PBE-UNet获取。

英文摘要：

Accurate lesion segmentation in ultrasound images is essential for preventive screening and clinical diagnosis, yet remains challenging due to low contrast, blurry boundaries, and significant scale variations. Although existing deep learning-based methods have achieved remarkable performance, these methods still struggle with scale variations and indistinct tumor boundaries. To address these challenges, we propose a progressive boundary enhanced U-Net (PBE-UNet). Specially, we first introduce a scale-aware aggregation module (SAAM) that dynamically adjusts its receptive field to capture robust multi-scale contextual information. Then, we propose a boundary-guided feature enhancement (BGFE) module to enhance the feature representations. We find that there are large gaps between the narrow boundary and the wide segmentation error areas. Unlike existing methods that treat boundaries as static masks, the BGFE module progressively expands the narrow boundary prediction into broader spatial attention maps. Thus, broader spatial attention maps could effectively cover the wider segmentation error regions and enhance the model's focus on these challenging areas. We conduct expensive experiments on four benchmark ultrasound datasets, BUSI, Dataset B, TN3K, and BP. The experimental results how that our proposed PBE-UNet outperforms state-of-the-art ultrasound image segmentation methods. The code is at https://github.com/cruelMouth/PBE-UNet.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。