We present a simple, yet effective, auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of Local Shape Descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors are designed to capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a large study comparing several existing methods across various specimen, imaging techniques, and resolutions, we find that auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (Flood-Filling Networks, FFN), while being two orders of magnitudes more efficient—a critical requirement for the processing of future petabyte-sized datasets. Implementations of the new auxiliary learning task, network architectures, training, prediction, and evaluation code, as well as the datasets used in this study are publicly available as a benchmark for future method contributions.

中文翻译：

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神经元分割的局部形状描述符

我们针对电子显微镜体积中的神经元分割问题提出了一个简单但有效的辅助学习任务。辅助任务包括局部形状描述符（LSD ）的预测)，我们将其与传统的体素直接相邻亲和性结合用于神经元边界检测。形状描述符旨在捕获有关要分割的神经元的局部统计数据，例如直径、伸长率和方向。在一项比较各种样本、成像技术和分辨率的现有方法的大型研究中，我们发现 LSD 的辅助学习在一系列指标上不断提高基于亲和力的方法的分割准确性。此外，LSD 的添加促进了基于亲和力的分割方法与当前最先进的神经元分割（Flood-Filling Networks，FFN）相提并论，同时效率提高了两个数量级——这是处理的关键要求未来 PB 级的数据集。新的辅助学习任务的实现，