We propose a novel Deep Active Learning (DeepAL) model-3D Wasserstein Discriminative UNet (WD-UNet) for reducing the annotation effort of medical 3D Computed Tomography (CT) segmentation. The proposed WD-UNet learns in a semi-supervised way and accelerates learning convergence to meet or exceed the prediction metrics of supervised learning models. Our method can be embedded with different Active Learning (AL) strategies and different network structures. The model is evaluated on 3D lung airway CT scans for medical segmentation and show that the use of uncertainty metric, which is parametrized as an input of query strategy, leads to more accurate prediction results than some state-of-the-art Deep Learning (DL) supervised models, e.g.,3DUNet and 3D CEUNet. Compared to the above supervised DL methods, our WD-UNet not only saves the cost of annotation for radiologists but also saves computational resources. WD-UNet uses a limited amount of annotated data (35% of the total) to achieve better predictive metrics with a more efficient deep learning model algorithm.

翻译：我们提出一种新颖的深度主动学习模型——三维Wasserstein判别UNet，以减少医学三维计算机断层扫描分割的标注工作量。该WD-UNet以半监督方式学习，并加速学习收敛，以达到或超越监督学习模型的预测指标。我们的方法可嵌入不同的主动学习策略和不同网络结构。该模型在用于医学分割的三维肺部气道CT扫描上进行评估，结果表明，采用不确定性度量（作为查询策略的输入参数化）能够比一些最先进的深度学习监督模型（如3D UNet和3D CEUNet）获得更准确的预测结果。与上述监督深度学习方法相比，我们的WD-UNet不仅为放射科医生节省了标注成本，还节省了计算资源。WD-UNet仅使用有限的标注数据（总量的35%），凭借更高效的深度学习模型算法即可实现更优预测指标。