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Since labeling medical image data is a costly and labor-intensive process,active learning has gained much popularity in the medical image segmentationdomain in recent years. A variety of active learning strategies have beenproposed in the literature, but their effectiveness is highly dependent on thedataset and training scenario. To facilitate the comparison of existingstrategies and provide a baseline for evaluating novel strategies, we evaluatethe performance of several well-known active learning strategies on threedatasets from the Medical Segmentation Decathlon. Additionally, we consider astrided sampling strategy specifically tailored to 3D image data. Wedemonstrate that both random and strided sampling act as strong baselines anddiscuss the advantages and disadvantages of the studied methods. To allow otherresearchers to compare their work to our results, we provide an open-sourceframework for benchmarking active learning strategies on a variety of medicalsegmentation datasets.

Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation