  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.
