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Serial Two-Photon Tomography of the Whole Marmoset Brain for Neuroanatomical Analyses
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对的神经元计数 半透镜大脑连接性研究

Zhenwei Dong, Xinyi Liu, Weiyang Shi

    IEEE transactions on medical imaging
    |November 4, 2025
    PubMed
    概括

    研究人员开发了一套新的数据集和人工智能模型,用于绘制大脑连接的地图. 这种工具精确地计数和定位神经元,推进了创造猿大脑连接体图谱的研究.

    科学领域:

    • 神经科学是一个神经科学.
    • 计算生物学 计算生物学
    • 数据科学数据科学数据科学

    背景情况:

    • 精确量化和定位标记标记神经元对于理解大脑连接和构建大脑连接体图谱至关重要.
    • 现有的方法在数据集开发和准确性方面面临挑战.

    研究的目的:

    • 介绍鱼光标记神经元 (MFN) 数据集用于神经元量化和定位.
    • 开发和验证一种先进的AI模型,用于精确地计算和定位大脑中的细胞.

    主要方法:

    • 从三只 rhesus 的逆行追踪创建了 MFN 数据集,包括 1,600 张图像和 33,411 个神经元注释.
    • 开发了一种集密卷积注意力U-Net (DAUNet) 模型,整合了集密卷积块和多尺度注意力模块.
    • 在MFN数据集和四个额外的公共数据集上验证了DAUNet.

    主要成果:

    • 在MFN数据集上,DAUNet实现了细胞计数的平均绝对误差为0.97,细胞定位的F1得分为96.29%.
    • 该模型在细胞计数和定位任务中表现优于几个基准模型.
    • DAUNet在多个数据集中展示了强大的概括能力.

    结论:

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    • 的MFN数据集和DAUNet模型提供了宝贵的资源,以推进的中视镜大脑连接研究.
    • 开发的模型成功量化了被标记的神经元,并绘制了大脑中的连接模式.
    • 这项工作有助于构建的大脑连接体.