Deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations

Advancements in volume electron microscopy mean it is now possible to generate thousands of serial images at nanometre resolution overnight, yet the gold standard approach for data analysis remains manual segmentation by an expert microscopist, resulting in a critical research bottleneck. Although some machine learning approaches exist in this domain, we remain far from realizing the aspiration of a highly accurate, yet generic, automated analysis approach, with a major obstacle being lack of sufficient high‐quality ground‐truth data. To address this, we developed a novel citizen science project, Etch a Cell, to enable volunteers to manually segment the nuclear envelope (NE) of HeLa cells imaged with serial blockface scanning electron microscopy. We present our approach for aggregating multiple volunteer annotations to generate a high‐quality consensus segmentation and demonstrate that data produced exclusively by volunteers can be used to train a highly accurate machine learning algorithm for automatic segmentation of the NE, which we share here, in addition to our archived benchmark data. Serial blockface scanning electron microscopy was used to create a serial HeLa cell image stack, A. The nuclear envelope (NE) in these data was segmented through volunteer effort via citizen science, B,C. High‐quality NE segmentations were produced, D, and used to train a deep learning model for automatic segmentation of the NE. Volunteer and deep learning predicted NE segmentations were comparable to expert date, E.