Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, United Kingdom.
PLoS One. 2011;6(10):e26007. doi: 10.1371/journal.pone.0026007. Epub 2011 Oct 7.
Tissue MicroArrays (TMAs) represent a potential high-throughput platform for the analysis and discovery of tissue biomarkers. As TMA slides are produced manually and subject to processing and sectioning artefacts, the layout of TMA cores on the final slide and subsequent digital scan (TMA digital slide) is often disturbed making it difficult to associate cores with their original position in the planned TMA map. Additionally, the individual cores can be greatly altered and contain numerous irregularities such as missing cores, grid rotation and stretching. These factors demand the development of a robust method for de-arraying TMAs which identifies each TMA core, and assigns them to their appropriate coordinates on the constructed TMA slide.
This study presents a robust TMA de-arraying method consisting of three functional phases: TMA core segmentation, gridding and mapping. The segmentation of TMA cores uses a set of morphological operations to identify each TMA core. Gridding then utilises a Delaunay Triangulation based method to find the row and column indices of each TMA core. Finally, mapping correlates each TMA core from a high resolution TMA whole slide image with its name within a TMAMap.
This study describes a genuine robust TMA de-arraying algorithm for the rapid identification of TMA cores from digital slides. The result of this de-arraying algorithm allows the easy partition of each TMA core for further processing. Based on a test group of 19 TMA slides (3129 cores), 99.84% of cores were segmented successfully, 99.81% of cores were gridded correctly and 99.96% of cores were mapped with their correct names via TMAMaps. The gridding of TMA cores were also extensively tested using a set of 113 pseudo slide (13,536 cores) with a variety of irregular grid layouts including missing cores, rotation and stretching. 100% of the cores were gridded correctly.
组织微阵列（Tissue MicroArrays，TMAs）代表了一种用于分析和发现组织生物标志物的高通量平台。由于 TMA 载玻片是手动制作的，并且受到处理和切片伪影的影响，因此最终载玻片上 TMA 芯的布局和随后的数字扫描（TMA 数字载玻片）经常会受到干扰，使得难以将芯与 TMA 图谱中原始位置相关联。此外，单个芯可能会发生很大的变化，并且包含许多不规则性，例如缺少芯、网格旋转和拉伸。这些因素要求开发一种强大的 TMA 去阵列方法，该方法可以识别每个 TMA 芯，并将它们分配到构建的 TMA 载玻片上的适当坐标。
本研究提出了一种强大的 TMA 去阵列方法，该方法由三个功能阶段组成：TMA 芯分割、网格和映射。TMA 芯的分割使用一组形态学操作来识别每个 TMA 芯。然后，网格利用基于 Delaunay 三角剖分的方法来找到每个 TMA 芯的行和列索引。最后，映射将高分辨率 TMA 全幻灯片图像中的每个 TMA 芯与其在 TMAMap 中的名称相关联。
本研究描述了一种真正强大的 TMA 去阵列算法，用于快速从数字幻灯片中识别 TMA 芯。该去阵列算法的结果允许轻松分割每个 TMA 芯以进行进一步处理。基于 19 个 TMA 载玻片（3129 个芯）的测试组，成功分割了 99.84%的芯，正确网格化了 99.81%的芯，并且通过 TMAMaps 将 99.96%的芯映射到正确的名称。还使用一组具有各种不规则网格布局的 113 个伪载玻片（13536 个芯）对 TMA 芯的网格化进行了广泛测试，包括缺少芯、旋转和拉伸。100%的芯都被正确网格化。
