Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning
This addresses the challenge of expensive labeling in medical imaging for clinicians, but it is incremental as it builds on existing semi-supervised methods with specific improvements.
The paper tackles the problem of medical instrument segmentation in 3D ultrasound for image-guided intervention by proposing a semi-supervised learning framework that reduces annotation effort, achieving Dice scores of 68.6%-69.1% and inference times of about 1 second per volume.
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.