Chen Wu, Liao Mingwei, Bao Shengda, An Sile, Li Wenwei, Liu Xin, Huang Ganghua, Gong Hui, Luo Qingming, Xiao Chi, Li Anan
Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China.
Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou 570228, China.
Patterns (N Y). 2024 Jun 21;5(8):101007. doi: 10.1016/j.patter.2024.101007. eCollection 2024 Aug 9.
Reconstructing neuronal morphology is vital for classifying neurons and mapping brain connectivity. However, it remains a significant challenge due to its complex structure, dense distribution, and low image contrast. In particular, AI-assisted methods often yield numerous errors that require extensive manual intervention. Therefore, reconstructing hundreds of neurons is already a daunting task for general research projects. A key issue is the lack of specialized training for challenging regions due to inadequate data and training methods. This study extracted 2,800 challenging neuronal blocks and categorized them into multiple density levels. Furthermore, we enhanced images using an axial continuity-based network that improved three-dimensional voxel resolution while reducing the difficulty of neuron recognition. Comparing the pre- and post-enhancement results in automatic algorithms using fluorescence micro-optical sectioning tomography (fMOST) data, we observed a significant increase in the recall rate. Our study not only enhances the throughput of reconstruction but also provides a fundamental dataset for tangled neuron reconstruction.
重建神经元形态对于神经元分类和绘制脑连接图谱至关重要。然而，由于其结构复杂、分布密集且图像对比度低，这仍然是一项重大挑战。特别是，人工智能辅助方法常常会产生大量错误，需要大量人工干预。因此，对于一般研究项目而言，重建数百个神经元已经是一项艰巨的任务。一个关键问题是，由于数据和训练方法不足，缺乏针对具有挑战性区域的专门训练。本研究提取了2800个具有挑战性的神经元块，并将它们分为多个密度级别。此外，我们使用基于轴向连续性的网络增强图像，提高了三维体素分辨率，同时降低了神经元识别的难度。使用荧光显微光学切片断层扫描（fMOST）数据，比较自动算法增强前后的结果，我们观察到召回率显著提高。我们的研究不仅提高了重建的通量，还为复杂神经元重建提供了一个基础数据集。
