德国马克斯·普朗克大脑研究所Moritz Helmstaedter、Martin Schmidt研究小组在研究中取得进展。他们研发了RoboEM——突触分辨率连接组学自动三维（3D）飞行示踪技术。这一研究成果发表在2024年3月21日出版的国际学术期刊《自然-方法学》上。

研究人员开发了一种基于人工智能的自转向三维"飞行"系统-RoboEM，该系统经过训练，仅使用三维-EM数据作为输入，就能沿着神经元示踪。应用于小鼠和人类大脑皮层的三维电子显微镜数据，RoboEM大大改进了最先进的自动分割技术，并能取代人工校对来处理更复杂的连接组分析问题，其大脑皮层连接组的计算注释成本比人工纠错成本约低400倍。

据了解，利用三维电子显微镜（3D-EM）数据绘制神经元网络图仍然面临着巨大挑战，尤其是对于细的轴突。目前可用的自动图像分割方法在许多类型的连接体分析中都需要人工校对。

附：英文原文

Title: RoboEM: automated 3D flight tracing for synaptic-resolution connectomics

Author: Schmidt, Martin, Motta, Alessandro, Sievers, Meike, Helmstaedter, Moritz

Issue&Volume: 2024-03-21

Abstract: Mapping neuronal networks from three-dimensional electron microscopy (3D-EM) data still poses substantial reconstruction challenges, in particular for thin axons. Currently available automated image segmentation methods require manual proofreading for many types of connectomic analysis. Here we introduce RoboEM, an artificial intelligence-based self-steering 3D ‘flight’ system trained to navigate along neurites using only 3D-EM data as input. Applied to 3D-EM data from mouse and human cortex, RoboEM substantially improves automated state-of-the-art segmentations and can replace manual proofreading for more complex connectomic analysis problems, yielding computational annotation cost for cortical connectomes about 400-fold lower than the cost of manual error correction.

DOI: 10.1038/s41592-024-02226-5

Source: https://www.nature.com/articles/s41592-024-02226-5