Accurate Automatic Detection of Densely Distributed Cell Nuclei in 3D Space.
Accurate Automatic Detection of Densely Distributed Cell Nuclei in 3D Space.
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DOI：
10.1371/journal.pcbi.1004970
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发表时间：
2016-06
影响因子：
4.3
通讯作者：
Iino Y
中科院分区：
文献类型：
--
作者：
Toyoshima Y;Tokunaga T;Hirose O;Kanamori M;Teramoto T;Jang MS;Kuge S;Ishihara T;Yoshida R;Iino Y
为了使用全脑活动成像来测量神经元的活动，需要精确地检测每个神经元或其核。在线虫头部，神经元胞体密集分布在三维空间。然而，目前还没有一种图像分析的计算方法能够足够准确地将它们分开。本文提出了一种基于等强度曲面曲率的高精度分割方法。为了获得准确的原子核位置，我们还开发了一种新的最小二乘法来拟合高斯混合模型。结合这些方法，能够在3D空间中准确地检测密集分布的细胞核。所提出的方法被实现为图形用户界面程序，允许可视化和校正自动检测的结果。此外，将该方法应用于时间推移的三维钙离子成像数据，成功地跟踪和测量了图像中的大部分核团。为了达到神经科学的最终目标，了解大脑中每个神经元的功能，具有单细胞分辨率的全脑活动成像技术已经被密集地开发出来。全脑图像中含有大量的神经元，人工检测这些神经元非常耗时。然而，神经元通常密集地分布在3D空间中，现有的自动方法无法正确地分割这些块。事实上，在之前关于线虫全脑活动成像的报道中，检测到的神经元数量少于预期。这种稀缺性可能是导致测量误差和神经元类别错误识别的一个原因。在这里，我们开发了一种高精度的密集细胞自动检测方法。提出的方法成功地检测到了线虫全脑图像中的几乎所有神经元。我们的方法可以用于跟踪多个目标，并能够从全脑活动成像数据中自动测量神经元活动。我们还开发了一个可视化和校正工具，对实验人员有帮助。此外，所提出的方法可以作为其他应用的基本技术，例如制作神经元的接线图或建立胚胎发育中的细胞谱系。因此，我们的框架支持有效和准确的生物图像分析。
To measure the activity of neurons using whole-brain activity imaging, precise detection of each neuron or its nucleus is required. In the head region of the nematode C. elegans, the neuronal cell bodies are distributed densely in three-dimensional (3D) space. However, no existing computational methods of image analysis can separate them with sufficient accuracy. Here we propose a highly accurate segmentation method based on the curvatures of the iso-intensity surfaces. To obtain accurate positions of nuclei, we also developed a new procedure for least squares fitting with a Gaussian mixture model. Combining these methods enables accurate detection of densely distributed cell nuclei in a 3D space. The proposed method was implemented as a graphical user interface program that allows visualization and correction of the results of automatic detection. Additionally, the proposed method was applied to time-lapse 3D calcium imaging data, and most of the nuclei in the images were successfully tracked and measured. To reach the ultimate goal of neuroscience to understanding how each neuron functions in the brain, whole-brain activity imaging techniques with single-cell resolution have been intensively developed. There are many neurons in the whole-brain images and manual detection of the neurons is very time-consuming. However, the neurons are often packed densely in the 3D space and existing automatic methods fail to correctly split the clumps. In fact, in previous reports of whole-brain activity imaging of C. elegans, the number of detected neurons were less than expected. Such scarcity may be a cause of measurement errors and misidentification of neuron classes. Here we developed a highly accurate automatic cell detection method for densely-packed cells. The proposed method successfully detected almost all neurons in whole-brain images of the nematode. Our method can be used to track multi-objects and enables automatic measurements of the neuronal activities from whole-brain activity imaging data. We also developed a visualization and correction tool that is helpful for experimenters. Additionally, the proposed method can be a fundamental technique for other applications such as making wiring diagram of neurons or establishing a cell lineage in embryonic development. Thus our framework supports effective and accurate bio-image analyses.
影响因子：
3.7
作者：
Bashar MK;Yamagata K;Kobayashi TJ
通讯作者：
Kobayashi TJ