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Random-forest-based automated cell detection in Knife-Edge Scanning Microscope rat Nissl data

机译:刀口扫描显微镜大鼠Nissl数据中基于随机森林的自动细胞检测

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Rapid advances in high-resolution, high-throughput 3D microscopy techniques in the past decade have opened up new avenues for brain research. One such technique developed in our lab is called the Knife-Edge Scanning Microscopy (KESM). The basic principle of KESM is to line-scan image while simultaneously sectioning thin tissue blocks using a diamond microtome. We have successfully sectioned and imaged whole mouse brains and portions of a rat brain processed with different stains to investigate the microstructures within. In this paper, we will present a fully automated soma (cell body) detection method based on random forests, working on Nissl-stained rat brain specimen. The method enables fast and accurate cell counting and density measurement in different brain regions.
机译:在过去的十年中,高分辨率,高通量3D显微镜技术的飞速发展为脑研究开辟了新途径。我们实验室开发的一种此类技术称为刀刃扫描显微镜(KESM)。 KESM的基本原理是线扫描图像,同时使用钻石切片机切片薄的组织块。我们已经成功地切片和成像了整个老鼠的大脑以及用不同的染色剂处理过的老鼠大脑的一部分,以研究其中的微观结构。在本文中,我们将提出一种基于随机森林的全自动体细胞(细胞体)检测方法,该方法适用于Nissl染色的大鼠脑标本。该方法能够在不同的大脑区域进行快速准确的细胞计数和密度测量。

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