Deep convolutional neural networks are widely used in medical image segmentation but require many labeled images for training. Annotating three-dimensional medical images is a time-consuming and costly process. To overcome this limitation, we propose a novel semi-supervised segmentation method that leverages mostly unlabeled images and a small set of labeled images in training. Our approach involves assessing prediction uncertainty to identify reliable predictions on unlabeled voxels from the teacher model. These voxels serve as pseudo-labels for training the student model. In voxels where the teacher model produces unreliable predictions, pseudo-labeling is carried out based on voxel-wise embedding correspondence using reference voxels from labeled images. We applied this method to automate hip bone segmentation in CT images, achieving notable results with just 4 CT scans. The proposed approach yielded a Hausdorff distance with 95th percentile (HD95) of 3.30 and IoU of 0.929, surpassing existing methods achieving HD95 (4.07) and IoU (0.927) at their best.

翻译：深度卷积神经网络在医学图像分割中应用广泛，但训练过程需要大量标注图像。三维医学图像的标注是耗时且成本高昂的过程。为克服这一限制，我们提出了一种新颖的半监督分割方法，该方法在训练中主要利用未标注图像和少量标注图像。我们的方法通过评估预测不确定性来识别教师模型对未标注体素产生的可靠预测。这些体素作为伪标签用于训练学生模型。在教师模型产生不可靠预测的体素区域，伪标签生成基于体素级嵌入匹配，使用来自标注图像的参考体素。我们将该方法应用于CT图像中髋骨的自动分割，仅使用4个CT扫描即取得显著成果。所提方法获得的豪斯多夫距离95百分位数（HD95）为3.30，交并比（IoU）为0.929，优于现有方法的最佳表现（HD95为4.07，IoU为0.927）。