IEEE Trans Med Imaging. 2023 Dec;42(12):3779-3793. doi: 10.1109/TMI.2023.3309249. Epub 2023 Nov 30.
Accurate ultrasound (US) image segmentation is crucial for the screening and diagnosis of diseases. However, it faces two significant challenges: 1) pixel-level annotation is a time-consuming and laborious process; 2) the presence of shadow artifacts leads to missing anatomy and ambiguous boundaries, which negatively impact reliable segmentation results. To address these challenges, we propose a novel semi-supervised shadow aware network with boundary refinement (SABR-Net). Specifically, we add shadow imitation regions to the original US, and design shadow-masked transformer blocks to perceive missing anatomy of shadow regions. Shadow-masked transformer block contains an adaptive shadow attention mechanism that introduces an adaptive mask, which is updated automatically to promote the network training. Additionally, we utilize unlabeled US images to train a missing structure inpainting path with shadow-masked transformer, which further facilitates semi-supervised segmentation. Experiments on two public US datasets demonstrate the superior performance of the SABR-Net over other state-of-the-art semi-supervised segmentation methods. In addition, experiments on a private breast US dataset prove that our method has a good generalization to clinical small-scale US datasets.
准确的超声（US）图像分割对于疾病的筛查和诊断至关重要。然而，它面临两个重大挑战：1）像素级注释是一个耗时且费力的过程；2）存在阴影伪影会导致解剖结构缺失和边界模糊，从而对可靠的分割结果产生负面影响。为了解决这些挑战，我们提出了一种新颖的具有边界细化功能的半监督阴影感知网络（SABR-Net）。具体来说，我们在原始 US 中添加了阴影模仿区域，并设计了阴影掩蔽的变压器块来感知阴影区域的缺失解剖结构。阴影掩蔽的变压器块包含自适应阴影注意力机制，该机制引入了一个自适应掩模，它会自动更新，以促进网络训练。此外，我们利用未标记的 US 图像来训练带有阴影掩蔽的变压器的缺失结构修复路径，从而进一步促进半监督分割。在两个公共 US 数据集上的实验表明，SABR-Net 优于其他最先进的半监督分割方法。此外，在一个私人乳腺 US 数据集上的实验证明了我们的方法对临床小数据集具有良好的泛化能力。
