Automated Cell Nuclei detection for Large-Volume Electron Microscopy of Neural Tissue

Volumetric electron microscopy techniques, such as serial block-face electron microscopy (SBEM), generate massive amounts of image data that are used for reconstructing neural circuits. Typically, this requires time-intensive manual annotation of cells and their connections. To facilitate this analysis, we study the problem of automated detection of cell nuclei in a new SBEM dataset that contains cerebral cortex, white matter, and striatum from an adult mouse brain. The dataset was manually annotated to identify the locations of all 3309 cell nuclei in the volume. We make both dataset and annotations available here. This is a supplementary to the ISBI 2014 paper "Automated Cell Nucleus Detection for Large-Volume Electron Microscopy of Neural Tissue".体积电子显微镜（volumetric electron microscopy）技术，例如连续块面电子显微镜（serial block-face electron microscopy，SBEM），可生成海量图像数据，用于神经环路重构。传统流程中，该类任务需要进行耗时费力的细胞及其连接人工标注。为简化此类分析工作，我们针对一个全新的SBEM数据集开展了细胞核自动检测任务的研究，该数据集包含成年小鼠大脑的大脑皮层、白质与纹状体区域。该数据集已完成人工标注，精准定位了全成像体积内全部3309个细胞核的空间位置。我们在此公开本数据集及其全部标注信息。 本文为发表于2014年国际生物医学影像研讨会（ISBI 2014）的论文《面向神经组织大体积电子显微镜成像的细胞核自动检测》的补充材料。