Medical instrument segmentation in 3D ultrasound is essential for image-guided intervention. However, to train a successful deep neural network for instrument segmentation, a large number of labeled images are required, which is expensive and time-consuming to obtain. In this article, we propose a semi-supervised learning (SSL) framework for instrument segmentation in 3D US, which requires much less annotation effort than the existing methods. To achieve the SSL learning, a Dual-UNet is proposed to segment the instrument. The Dual-UNet leverages unlabeled data using a novel hybrid loss function, consisting of uncertainty and contextual constraints. Specifically, the uncertainty constraints leverage the uncertainty estimation of the predictions of the UNet, and therefore improve the unlabeled information for SSL training. In addition, contextual constraints exploit the contextual information of the training images, which are used as the complementary information for voxel-wise uncertainty estimation. Extensive experiments on multiple ex-vivo and in-vivo datasets show that our proposed method achieves Dice score of about 68.6%-69.1% and the inference time of about 1 sec. per volume. These results are better than the state-of-the-art SSL methods and the inference time is comparable to the supervised approaches.

翻译：3D 超声波中的医学仪器分解是图像引导干预的关键。 但是,为了培训成功的深神经网络进行仪器分解,需要大量贴标签图像,这需要花费大量时间和费用。在本篇文章中,我们提议为3D 美国的仪器分解建立一个半监督学习(SSL)框架,这比现有方法少得多的说明努力。为了实现SSL的学习,建议使用双联合国软件来分割仪器。双联合国软件利用无标签数据,使用由不确定性和背景限制组成的新型混合损失功能。具体地说,不确定性制约利用了UNet预测的不确定性估计,从而改进了SSL培训的无标签信息。此外,背景限制利用了培训图像的背景信息,而这种信息是用于对Voxel的不确定性估计的补充信息。 多次前vivo 和 Vive数据集的广泛实验表明,我们拟议的方法取得了大约68.6%-69.1%的Dice分数,而SLV在1 秒中采用的推算时间比SL方法要好。