医学超声图像分割中的多尺度半监督与对比学习切换机制

摘要

医学超声图像分割面临标注数据有限及固有成像伪影（包括斑点噪声和低对比度边界）带来的重大挑战。尽管半监督学习方法已开始应对数据稀缺问题，但现有方法存在未标注数据利用欠佳、缺乏鲁棒特征表示机制等不足。本文提出Switch这一新型半监督学习框架，其具备两项核心创新：(1) 多尺度切换策略，通过分层块混合实现均匀空间覆盖；(2) 频域切换机制，结合对比学习在傅里叶空间执行幅度切换以获取鲁棒特征表示。本框架将上述组件集成于师生架构中，有效协同利用标注与未标注数据。在六个多样化超声数据集（淋巴结、乳腺病灶、甲状腺结节及前列腺）上的综合评估表明，该方法持续优于现有最优技术。在5%标注比例下，Switch取得显著提升：LN-INT数据集Dice系数达80.04%，DDTI数据集达85.52%，前列腺数据集达83.48%，其半监督性能甚至超越全监督基线。该方法在保持参数高效性（180万参数）的同时提供卓越性能，验证了其在资源受限的医学影像应用中的有效性。源代码已公开于https://github.com/jinggqu/Switch。

English

Medical ultrasound image segmentation faces significant challenges due to limited labeled data and characteristic imaging artifacts including speckle noise and low-contrast boundaries. While semi-supervised learning (SSL) approaches have emerged to address data scarcity, existing methods suffer from suboptimal unlabeled data utilization and lack robust feature representation mechanisms. In this paper, we propose Switch, a novel SSL framework with two key innovations: (1) Multiscale Switch (MSS) strategy that employs hierarchical patch mixing to achieve uniform spatial coverage; (2) Frequency Domain Switch (FDS) with contrastive learning that performs amplitude switching in Fourier space for robust feature representations. Our framework integrates these components within a teacher-student architecture to effectively leverage both labeled and unlabeled data. Comprehensive evaluation across six diverse ultrasound datasets (lymph nodes, breast lesions, thyroid nodules, and prostate) demonstrates consistent superiority over state-of-the-art methods. At 5\% labeling ratio, Switch achieves remarkable improvements: 80.04\% Dice on LN-INT, 85.52\% Dice on DDTI, and 83.48\% Dice on Prostate datasets, with our semi-supervised approach even exceeding fully supervised baselines. The method maintains parameter efficiency (1.8M parameters) while delivering superior performance, validating its effectiveness for resource-constrained medical imaging applications. The source code is publicly available at https://github.com/jinggqu/Switch

医学超声图像分割中的多尺度半监督与对比学习切换机制

Multiscale Switch for Semi-Supervised and Contrastive Learning in Medical Ultrasound Image Segmentation

摘要

Support