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Over the last decade, electron microscopy has improved up to a point thatgenerating high quality gigavoxel sized datasets only requires a few hours.Automated image analysis, particularly image segmentation, however, has notevolved at the same pace. Even though state-of-the-art methods such as U-Netand DeepLab have improved segmentation performance substantially, the requiredamount of labels remains too expensive. Active learning is the subfield inmachine learning that aims to mitigate this burden by selecting the samplesthat require labeling in a smart way. Many techniques have been proposed,particularly for image classification, to increase the steepness of learningcurves. In this work, we extend these techniques to deep CNN based imagesegmentation. Our experiments on three different electron microscopy datasetsshow that active learning can improve segmentation quality by 10 to 15% interms of Jaccard score compared to standard randomized sampling.

Cost-efficient segmentation of electron microscopy images using active learning