Segmentation of anatomical structures is a fundamental image analysis task for many applications in the medical field. Deep learning methods have been shown to perform well, but for this purpose large numbers of manual annotations are needed in the first place, which necessitate prohibitive levels of resources that are often unavailable. In an active learning framework of selecting informed samples for manual labeling, expert clinician time for manual annotation can be optimally utilized, enabling the establishment of large labeled datasets for machine learning. In this paper, we propose a novel method that combines representativeness with uncertainty in order to estimate ideal samples to be annotated, iteratively from a given dataset. Our novel representativeness metric is based on Bayesian sampling, by using information-maximizing autoencoders. We conduct experiments on a shoulder magnetic resonance imaging (MRI) dataset for the segmentation of four musculoskeletal tissue classes. Quantitative results show that the annotation of representative samples selected by our proposed querying method yields an improved segmentation performance at each active learning iteration, compared to a baseline method that also employs uncertainty and representativeness metrics. For instance, with only 10% of the dataset annotated, our method reaches within 5% of Dice score expected from the upper bound scenario of all the dataset given as annotated (an impractical scenario due to resource constraints), and this gap drops down to a mere 2% when less than a fifth of the dataset samples are annotated. Such active learning approach to selecting samples to annotate enables an optimal use of the expert clinician time, being often the bottleneck in realizing machine learning solutions in medicine.

解剖结构的分割是医学领域许多应用的基本图像分析任务。深度学习方法已被证明表现良好，但是为此，首先需要大量的手动注释，这需要经常无法使用的资源水平过高。在选择用于手动标记的已知样本的主动学习框架中，可以最佳地利用专家临床医生的手动标记时间，从而可以建立大型标记的数据集进行机器学习。在本文中，我们提出了一种将代表性与不确定性相结合的新方法为了从给定的数据集中迭代地估计理想的样本。我们新颖的代表性度量基于贝叶斯采样，使用了信息最大化的自动编码器。我们在肩部磁共振成像（MRI）数据集上进行了四个肌肉骨骼组织类别细分的实验。定量结果表明，与也采用不确定性和代表性指标的基准方法相比，通过我们提出的查询方法选择的代表性样本的注释在每次主动学习迭代中均产生了改进的分割性能。例如，仅注释了10％的数据集，我们的方法就达到了Dice得分的5％以内，Dice分数是从所有以注释方式给出的数据集的上限场景中预期的（由于资源限制而无法实现），并且当注释少于五分之一的数据集样本时，该差距下降到仅2％。这种选择样本进行注释的主动学习方法可以最佳地利用专家临床医生的时间，而这通常是实现医学机器学习解决方案的瓶颈。