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Reconstruction of Vessel Structures from Serial Whole Slide Sections of Murine Liver Samples

机译:鼠肝样品串行整体载玻片血管结构的重建

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Image-based analysis of the vascular structures of murine liver samples is an important tool for scientists to understand liver physiology and morphology. Typical assessment methods are MicroCT, which allows for acquiring images of the whole organ while lacking resolution for fine details, and confocal laser scanning microscopy, which allows detailed insights into fine structures while lacking the broader context. Imaging of histological serial whole slide sections is a recent technology able to fill this gap, since it provides a fine resolution up to the cellular level, but on a whole organ scale. However, whole slide imaging is a modality providing only 2D images. Therefore the challenge is to use stacks of serial sections from which to reconstruct the 3D vessel structures. In this paper we present a semi-automatic procedure to achieve this goal. We employ an automatic method that detects vessel structures based on continuity and shape characteristics. Furthermore it supports the user to perform manual corrections where required. With our methods we were able to successfully extract and reconstruct vessel structures from a stack of 100 and a stack of 397 serial sections of a mouse liver lobe, thus proving the potential of our approach.
机译:基于图像的鼠肝样本的血管结构分析是科学家了解肝脏生理学和形态的重要工具。典型的评估方法是MIGRCT,其允许获取整个器官的图像,同时缺乏用于细细节的分辨率和共聚焦激光扫描显微镜,这允许详细的洞察细结构,同时缺乏更广泛的背景。组织学串行整个幻灯片部分的成像是最近能够填充这种差距的技术,因为它提供了较好的分辨率,直到蜂窝水平,而是在整个器官规模上。然而,整个幻灯片成像是提供仅2D图像的模态。因此,挑战是使用堆叠的串行部分来重建3D血管结构。在本文中,我们提出了一个半自动程序来实现这一目标。我们采用自动方法,可根据连续性和形状特性检测血管结构。此外,它支持用户在需要的情况下执行手动校正。通过我们的方法,我们能够从100的堆叠中成功提取和重建血管结构和小鼠肝叶的397个序列部分的堆栈结构,从而证明了我们方法的潜力。

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