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

TL;DR

This paper compares various active learning strategies for 3D medical image segmentation, highlighting their effectiveness across datasets and introducing a strided sampling baseline, supported by an open-source benchmarking framework.

Contribution

It provides a comprehensive comparison of active learning methods for 3D medical segmentation and introduces a tailored strided sampling strategy as a strong baseline.

Findings

Random and strided sampling are strong baselines.

Effectiveness of strategies varies with dataset and scenario.

Open-source benchmarking framework is provided.

Abstract

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.