Inria Rennes - Bretagne Atlantique, Campus universitaire de Beaulieu, Rennes, 35042, France.
Innopsys, Parc d'Activités Activestre, Carbonne, 31390, France.
BMC Bioinformatics. 2018 Apr 19;19(1):148. doi: 10.1186/s12859-018-2111-8.
Over the last two decades, an innovative technology called Tissue Microarray (TMA), which combines multi-tissue and DNA microarray concepts, has been widely used in the field of histology. It consists of a collection of several (up to 1000 or more) tissue samples that are assembled onto a single support - typically a glass slide - according to a design grid (array) layout, in order to allow multiplex analysis by treating numerous samples under identical and standardized conditions. However, during the TMA manufacturing process, the sample positions can be highly distorted from the design grid due to the imprecision when assembling tissue samples and the deformation of the embedding waxes. Consequently, these distortions may lead to severe errors of (histological) assay results when the sample identities are mismatched between the design and its manufactured output. The development of a robust method for de-arraying TMA, which localizes and matches TMA samples with their design grid, is therefore crucial to overcome the bottleneck of this prominent technology.
In this paper, we propose an Automatic, fast and robust TMA De-arraying (ATMAD) approach dedicated to images acquired with brightfield and fluorescence microscopes (or scanners). First, tissue samples are localized in the large image by applying a locally adaptive thresholding on the isotropic wavelet transform of the input TMA image. To reduce false detections, a parametric shape model is considered for segmenting ellipse-shaped objects at each detected position. Segmented objects that do not meet the size and the roundness criteria are discarded from the list of tissue samples before being matched with the design grid. Sample matching is performed by estimating the TMA grid deformation under the thin-plate model. Finally, thanks to the estimated deformation, the true tissue samples that were preliminary rejected in the early image processing step are recognized by running a second segmentation step.
We developed a novel de-arraying approach for TMA analysis. By combining wavelet-based detection, active contour segmentation, and thin-plate spline interpolation, our approach is able to handle TMA images with high dynamic, poor signal-to-noise ratio, complex background and non-linear deformation of TMA grid. In addition, the deformation estimation produces quantitative information to asset the manufacturing quality of TMAs.
在过去的二十年中，一种名为组织微阵列（TMA）的创新技术得到了广泛应用，该技术结合了多组织和 DNA 微阵列的概念。它由一组（多达 1000 个或更多）组织样本组成，根据设计网格（阵列）布局组装到单个载体上-通常是载玻片上，以便在相同和标准化的条件下对多个样本进行多路分析。然而，在 TMA 制造过程中，由于组装组织样本时的不精确性和嵌入蜡的变形，样本位置可能会严重偏离设计网格。因此，当设计与其制造输出之间的样本身份不匹配时，这些扭曲可能会导致（组织学）测定结果的严重误差。因此，开发一种强大的 TMA 去阵列方法，定位和匹配 TMA 样本及其设计网格，对于克服这项突出技术的瓶颈至关重要。
在本文中，我们提出了一种专用于明场和荧光显微镜（或扫描仪）采集图像的自动、快速和鲁棒的 TMA 去阵列（ATMAD）方法。首先，通过在输入 TMA 图像的各向同性小波变换上应用局部自适应阈值处理，在大图中定位组织样本。为了减少误检，在每个检测到的位置考虑使用参数形状模型来分割椭圆形状的对象。在与设计网格匹配之前，从组织样本列表中丢弃不符合大小和圆度标准的分割对象。通过在薄板模型下估计 TMA 网格变形来进行样本匹配。最后，通过运行第二个分割步骤，利用估计的变形识别在早期图像处理步骤中初步拒绝的真实组织样本。
我们开发了一种用于 TMA 分析的新型去阵列方法。通过结合基于小波的检测、主动轮廓分割和薄板样条插值，我们的方法能够处理具有高动态、低信噪比、复杂背景和 TMA 网格非线性变形的 TMA 图像。此外，变形估计产生定量信息来评估 TMAs 的制造质量。
