5-D干细胞图像序列自动分割、跟踪和谱系化的可视化和校正
Visualization and Correction of Automated Segmentation, Tracking and Lineaging from 5-D Stem Cell Image Sequences
摘要：
结果如下：我们提出了一个应用程序，使多通道5-D（x，y，z，t，通道）和大蒙太奇共聚焦荧光显微镜图像的定量分析。图像序列显示了干细胞与血管，使干细胞的动态行为与其血管生态位的量化，在发育和癌症生物学的应用。我们的应用程序自动分割，跟踪和谱系的图像序列数据，然后允许用户查看和编辑自动算法的结果，在一个立体的3-D窗口，同时查看干细胞谱系树在一个2-D窗口。使用GPU来存储和渲染图像序列数据实现了混合计算方法。一种基于推理的方法，利用用户提供的编辑来自动纠正相关错误，在GPU处理3D可视化任务的同时，在系统CPU上交互执行。结论：通过利用商品计算机游戏硬件，我们开发了一个可以在实验室运行的应用程序，以促进通过生物实验的快速迭代。有一个迫切需要的可视化和分析工具的5-D活细胞图像数据。我们将精确的无监督过程与直观的结果可视化相结合。我们的验证界面允许将每个数据集校正到100%的准确度，确保下游数据分析准确且可验证。我们的工具是第一个结合所有这些方面，利用立体可视化的验证信息，以提高低层次的图像处理任务，利用所获得的协同作用。
英文摘要：
Results: We present an application that enables the quantitative analysis of multichannel 5-D (x, y, z, t, channel) and large montage confocal fluorescence microscopy images. The image sequences show stem cells together with blood vessels, enabling quantification of the dynamic behaviors of stem cells in relation to their vascular niche, with applications in developmental and cancer biology. Our application automatically segments, tracks, and lineages the image sequence data and then allows the user to view and edit the results of automated algorithms in a stereoscopic 3-D window while simultaneously viewing the stem cell lineage tree in a 2-D window. Using the GPU to store and render the image sequence data enables a hybrid computational approach. An inference-based approach utilizing user-provided edits to automatically correct related mistakes executes interactively on the system CPU while the GPU handles 3-D visualization tasks. Conclusions: By exploiting commodity computer gaming hardware, we have developed an application that can be run in the laboratory to facilitate rapid iteration through biological experiments. There is a pressing need for visualization and analysis tools for 5-D live cell image data. We combine accurate unsupervised processes with an intuitive visualization of the results. Our validation interface allows for each data set to be corrected to 100% accuracy, ensuring that downstream data analysis is accurate and verifiable. Our tool is the first to combine all of these aspects, leveraging the synergies obtained by utilizing validation information from stereo visualization to improve the low level image processing tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
