Deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations
Deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations
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DOI：
10.1111/tra.12789
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发表时间：
2021-05-16
期刊：
影响因子：
4.5
通讯作者：
Jones, Martin L.
中科院分区：
文献类型：
--
作者：
Spiers, Helen;Songhurst, Harry;Jones, Martin L.
体积电子显微镜的进步意味着现在可以在一夜之间生成数千个纳米分辨率的连续图像，但数据分析的黄金标准方法仍然是由专家显微镜进行手动分割，这导致了关键的研究瓶颈。虽然在这个领域存在一些机器学习方法，但我们仍然远远没有实现高度准确但通用的自动化分析方法的愿望，主要障碍是缺乏足够的高质量地面实况数据。为了解决这个问题，我们开发了一个新颖的公民科学项目“Etch a Cell”，使志愿者能够手动分割用连续块面扫描电子显微镜成像的HeLa细胞的核被膜（NE）。我们提出了我们的方法，用于聚合多个志愿者注释以生成高质量的共识分割，并证明志愿者专门产生的数据可用于训练高度准确的机器学习算法，用于自动分割NE，我们在这里分享，除了我们存档的基准数据。
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.