Since labeling medical image data is a costly and labor-intensive process, active learning has gained much popularity in the medical image segmentation domain in recent years. A variety of active learning strategies have been proposed in the literature, but their effectiveness is highly dependent on the dataset and training scenario. To facilitate the comparison of existing strategies and provide a baseline for evaluating novel strategies, we evaluate the performance of several well-known active learning strategies on three datasets from the Medical Segmentation Decathlon. Additionally, we consider a strided sampling strategy specifically tailored to 3D image data. We demonstrate that both random and strided sampling act as strong baselines and discuss the advantages and disadvantages of the studied methods. To allow other researchers to compare their work to our results, we provide an open-source framework for benchmarking active learning strategies on a variety of medical segmentation datasets.

本文针对医学图像分割，通过评估多种主流主动学习策略在三个数据集上的表现，并提出针对3D图像数据的分层采样策略来作为评估基准，发现随机采样和分层采样对于医学图像分割来说是强有力的评估指标，同时探讨其优缺点，并提供了一个开源框架供其他研究者在多个医学图像分割数据集上进行主动学习策略的基准测试。

少即是多：3D医学图像分割的主动学习策略比较