Quachtran Benjamin, de la Torre Ubieta Luis, Yusupova Marianna, Geschwind Daniel H, Shattuck David W
Department of Neurology, David Geffen School of Medicine, UCLA.
Program in Neurogenetics, Departments of Neurology and Human Genetics, David Geffen School of Medicine, UCLA.
Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:658-662. doi: 10.1109/ISBI.2018.8363660. Epub 2018 May 24.
New tissue-clearing techniques and improvements in optical microscopy have rapidly advanced capabilities to acquire volumetric imagery of neural tissue at resolutions of one micron or better. As sizes for data collections increase, accurate automatic segmentation of cell nuclei becomes increasingly important for quantitative analysis of imaged tissue. We present a cell nucleus segmentation method that is formulated as a parameter estimation problem with the goal of determining the count, shapes, and locations of nuclei that most accurately describe an image. We applied our new voting-based approach to fluorescence confocal microscopy images of neural tissue stained with DAPI, which highlights nuclei. Compared to manual counting of cells in three DAPI images, our method outperformed three existing approaches. On a manually labeled high-resolution DAPI image, our method also outperformed those methods and achieved a cell count accuracy of 98.99% and mean Dice coefficient of 0.6498.
新的组织透明化技术和光学显微镜的改进迅速提升了以一微米或更高分辨率获取神经组织体积图像的能力。随着数据收集规模的增大，细胞核的精确自动分割对于成像组织的定量分析变得越来越重要。我们提出了一种细胞核分割方法，该方法被表述为一个参数估计问题，目标是确定最准确描述图像的细胞核数量、形状和位置。我们将基于投票的新方法应用于用DAPI染色的神经组织荧光共聚焦显微镜图像，DAPI能突出细胞核。与手动计数三张DAPI图像中的细胞相比，我们的方法优于三种现有方法。在一张手动标注的高分辨率DAPI图像上，我们的方法也优于那些方法，细胞计数准确率达到98.99%，平均骰子系数为0.6498。
