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TeraStitcher - A tool for fast automatic 3D-stitching of teravoxel-sized microscopy images

机译:TeraStitcher-用于对teravoxel尺寸的显微图像进行快速自动3D缝合的工具

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Background Further advances in modern microscopy are leading to teravoxel-sized tiled 3D images at high resolution, thus increasing the dimension of the stitching problem of at least two orders of magnitude. The existing software solutions do not seem adequate to address the additional requirements arising from these datasets, such as the minimization of memory usage and the need to process just a small portion of data. Results We propose a free and fully automated 3D Stitching tool designed to match the special requirements coming out of teravoxel-sized tiled microscopy images that is able to stitch them in a reasonable time even on workstations with limited resources. The tool was tested on teravoxel-sized whole mouse brain images with micrometer resolution and it was also compared with the state-of-the-art stitching tools on megavoxel-sized publicy available datasets. This comparison confirmed that the solutions we adopted are suited for stitching very large images and also perform well on datasets with different characteristics. Indeed, some of the algorithms embedded in other stitching tools could be easily integrated in our framework if they turned out to be more effective on other classes of images. To this purpose, we designed a software architecture which separates the strategies that use efficiently memory resources from the algorithms which may depend on the characteristics of the acquired images. Conclusions TeraStitcher is a free tool that enables the stitching of Teravoxel-sized tiled microscopy images even on workstations with relatively limited resources of memory (
机译:背景技术现代显微镜的进一步发展导致高分辨率的Teravoxel大小的平铺3D图像,从而使缝合问题的尺寸至少增加了两个数量级。现有的软件解决方案似乎不足以满足这些数据集带来的额外要求,例如最小化内存使用以及仅需要处理一小部分数据的需求。结果我们提出了一种免费的,全自动的3D拼接工具,旨在满足teravoxel尺寸平铺显微镜图像提出的特殊要求,即使在资源有限的工作站上,也可以在合理的时间内对其进行拼接。该工具已在具有微米分辨率的teravoxel大小的整个小鼠大脑图像上进行了测试,并与megavoxel大小的公开可用数据集上的最新缝合工具进行了比较。这种比较证实了我们采用的解决方案适用于拼接非常大的图像,并且在具有不同特征的数据集上也能很好地执行。的确,如果其他拼接工具中嵌入的某些算法对其他类别的图像更有效,则可以轻松地将其集成到我们的框架中。为此,我们设计了一种软件体系结构,该体系结构将有效使用内存资源的策略与可能取决于所获取图像特征的算法分开。结论TeraStitcher是一个免费工具,即使在内存资源相对有限的工作站上,也可以缝合Teravoxel大小的平铺显微镜图像(

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