Precise ultrasound segmentation is vital for clinicians to provide comprehensive diagnoses. However, developing a model that accurately segments ultrasound images is challenging due to the images' low quality and the scarcity of extensive labeled data. This results in two main solutions: (1) optimizing multi-scale feature representations, and (2) increasing resistance to data dependency. The first approach necessitates an advanced network architecture, but a handcrafted network is knowledge-intensive and often yields limited improvement. In contrast, neural architecture search (NAS) can more easily attain optimal performance, albeit with significant computational costs. Regarding the second issue, semi-supervised learning (SSL) is an established method, but combining it with complex NAS faces the risk of overfitting to a few labeled samples without extra constraints. Therefore, we introduce a hybrid constraint-driven semi-supervised Transformer-NAS (HCS-TNAS), balancing both solutions for segmentation. HCS-TNAS includes an Efficient NAS-ViT module for multi-scale token search before ViT's attention calculation, effectively capturing contextual and local information with lower computational costs, and a hybrid SSL framework that adds network independence and contrastive learning to the optimization for solving data dependency. By further developing a stage-wise optimization strategy, a rational network structure is identified. Experiments on public datasets show that HCS-TNAS achieves state-of-the-art performance, pushing the limit of ultrasound segmentation.

翻译：精确的超声分割对于临床医生提供全面诊断至关重要。然而，由于图像质量低以及大量标注数据的稀缺，开发能够准确分割超声图像的模型具有挑战性。这催生了两种主要解决方案：（1）优化多尺度特征表示，以及（2）增强对数据依赖性的鲁棒性。第一种方法需要先进的网络架构，但手工设计的网络知识密集且改进往往有限。相比之下，神经架构搜索（NAS）虽计算成本高昂，却能更轻松地达到最优性能。针对第二个问题，半监督学习（SSL）是一种成熟的方法，但将其与复杂的NAS结合时，若无额外约束，则面临对少量标注样本过拟合的风险。因此，我们提出了一种混合约束驱动的半监督Transformer-NAS（HCS-TNAS），以平衡这两种分割解决方案。HCS-TNAS包含一个高效的NAS-ViT模块，用于在ViT的注意力计算前进行多尺度令牌搜索，从而以较低计算成本有效捕获上下文和局部信息；以及一个混合SSL框架，该框架在优化中加入了网络独立性和对比学习，以解决数据依赖性问题。通过进一步开发分阶段优化策略，我们确定了一个合理的网络结构。在公开数据集上的实验表明，HCS-TNAS实现了最先进的性能，推动了超声分割的极限。