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首页> 外文期刊>Journal of chemical neuroanatomy >Automatic stereology of mean nuclear size of neurons using an active contour framework
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Automatic stereology of mean nuclear size of neurons using an active contour framework

机译：使用活跃轮廓框架的神经元的平均核大小的自动态度

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摘要

The use of unbiased stereology to quantify structural parameters such as mean cell and nuclear size (area and volume) can be useful for a wide variety of biological studies. Here we propose a novel segmentation framework using an Active Contour Model to automate the collection of stereology from stained cells and other objects in tissue sections. This approach is demonstrated for stained brain sections from young adult Fischer 344 rats. Animals were perfused in-vivo with 4% paraformaldehyde and sectioned by frozen microtomy at an instrument setting of 40 mu m. For each rat brain, a systematic-random set of sections through the entire substantia nigra pars compacta (SN) were immunostained to reveal tyrosine hydroxylase (TH)-immunopositive neurons. The novel framework applied an active contour (modified balloon snake) model with non-constant balloon force to automatically segment and quantify neuronal cell bodies by stereological point counting (SPC). Several contours were initialized in the image and based on the contour fit after 200 iterations classified as immunopositive (signal) or background contours in a sequential manner. Cell contours were determined in four steps based on several criteria, e.g., area of contour, dispersion measure, and degree of overlap. The image was automatically segmented according to the final contours. Using a point grid automatically generated at systematic-random orientations over the images, points hitting the segmented neural cell bodies were automatically counted. The final values from the automatic framework were compared with findings for ground truth (manual SPC). The results of this study show a strong agreement between data collected by the automatic framework and the ground truth (R-2 >= 0.95) with a 5x gain in time efficiency for the automatic SPC. These findings give strong support for future applications of pattern recognition for assessing stereological parameters of biological objects identified by high signal:noise stains.
机译：使用非偏见的立体管来量化结构参数，例如平均细胞和核尺寸（面积和体积）可用于各种各样的生物学研究。在这里，我们提出了一种使用活动轮廓模型提出了一种新颖的分段框架，以自动从染色的细胞和组织切片中的其他物体的集合。从年轻成人费车344大鼠中染色的脑切片证明了这种方法。将动物灌注于体内用4％多聚甲醛，并在40μm的仪器设置下通过冷冻的微豆细胞切片。对于每个大鼠脑，通过整个Implica NIGRA PARSCACTA（SN）的系统随机组部分被免疫染色，以显示酪氨酸羟化酶（TH）-immunopositive神经元。该小说框架应用了具有非恒定球囊力的有源轮廓（改进的气球蛇）模型，通过立体学点计数（SPC）自动分段和量化神经元细胞体。在图像中初始化几个轮廓，并基于以连续方式分类为免疫阳性（信号）或背景轮廓的200次迭代之后的轮廓拟合。基于若干标准，例如轮廓区域，分散测量和重叠程度的若干步骤，在四个步骤中确定细胞轮廓。根据最终轮廓自动分段图像。使用在图像上自动生成的点网格在图像上以系统随机取向产生，自动计算点击分段的神经单元体的点。将自动框架的最终值与地面真理（手动SPC）的调查结果进行比较。本研究的结果显示了自动框架收集的数据与地面真理（R-2> = 0.95）之间的强烈一致性，为自动SPC的时间效率为5倍。这些调查结果强大支持对通过高信号鉴定的生物物体的立体学参数进行模式识别的未来应用的强大支持：噪音污渍。

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