Allenby Mark C, Misener Ruth, Panoskaltsis Nicki, Mantalaris Athanasios
1 Biological Systems Engineering Laboratory, Department of Chemical Engineering, Imperial College London , London, United Kingdom .
2 Department of Computing, Imperial College London , London, United Kingdom .
Tissue Eng Part C Methods. 2017 Feb;23(2):108-117. doi: 10.1089/ten.TEC.2016.0413.
Three-dimensional (3D) imaging techniques provide spatial insight into environmental and cellular interactions and are implemented in various fields, including tissue engineering, but have been restricted by limited quantification tools that misrepresent or underutilize the cellular phenomena captured. This study develops image postprocessing algorithms pairing complex Euclidean metrics with Monte Carlo simulations to quantitatively assess cell and microenvironment spatial distributions while utilizing, for the first time, the entire 3D image captured. Although current methods only analyze a central fraction of presented confocal microscopy images, the proposed algorithms can utilize 210% more cells to calculate 3D spatial distributions that can span a 23-fold longer distance. These algorithms seek to leverage the high sample cost of 3D tissue imaging techniques by extracting maximal quantitative data throughout the captured image.
三维（3D）成像技术能够深入洞察环境与细胞间的相互作用，并已在包括组织工程在内的多个领域得到应用，但一直受到有限量化工具的限制，这些工具会错误呈现或未充分利用所捕获的细胞现象。本研究开发了图像后处理算法，将复杂的欧几里得度量与蒙特卡罗模拟相结合，以定量评估细胞和微环境的空间分布，同时首次利用所捕获的完整3D图像。尽管当前方法仅分析所呈现的共聚焦显微镜图像的中心部分，但所提出的算法可以利用多210%的细胞来计算3D空间分布，其跨度距离可延长23倍。这些算法旨在通过在整个捕获图像中提取最大量的定量数据，来充分利用3D组织成像技术的高样本成本。
