首页> 外文OA文献 >ATMAD : robust image analysis for Automatic Tissue MicroArray De-arraying
【2h】

ATMAD : robust image analysis for Automatic Tissue MicroArray De-arraying

机译:ATMAD:用于自动组织微阵列解阵列的强大图像分析

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

Background. 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 ofhistology. It consists of a collection of several (up to 1000 or more) tissue samples that are assembledonto a single support – typically a glass slide – according to a design grid (array) layout, in order toallow multiplex analysis by treating numerous samples under identical and standardized conditions.However, during the TMA manufacturing process, the sample positions can be highly distorted fromthe design grid due to the imprecision when assembling tissue samples and the deformation of theembedding waxes. Consequently, these distortions may lead to severe errors of (histological) assayresults 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 TMAsamples with their design grid, is therefore crucial to overcome the bottleneck of this prominenttechnology.Results. In this paper, we propose an Automatic, fast and robust TMA De-arraying (ATMAD)approach dedicated to images acquired with bright field and fluorescence microscopes (or scanners).First, tissue samples are localized in the large image by applying a locally adaptive thresholdingon the isotropic wavelet transform of the input TMA image. To reduce false detections, a parametricshape 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 thelist of tissue samples before being matched with the design grid. Sample matching is performed byestimating the TMA grid deformation under the thin-plate model. Finally, thanks to the estimateddeformation, the true tissue samples that were preliminary rejected in the early image processingstep are recognized by running a second segmentation step.Conclusions. We developed a novel de-arraying approach for TMA analysis. By combining waveletbaseddetection, active contour segmentation, and thin-plate spline interpolation, our approach isable to handle TMA images with high dynamic, poor signal-to-noise ratio, complex background andnon-linear deformation of TMA grid. In addition, the deformation estimation produces quantitativeinformation to asset the manufacturing quality of TMAs.
机译:背景。在过去的二十年中,结合了多组织和DNA微阵列概念的一种称为组织微阵列(TMA)的创新技术已被广泛用于组织学领域。它由几个(最多1000个或更多)组织样本的集合组成,这些样本根据设计网格(阵列)布局组装到单个支架(通常是载玻片)上,以便通过在相同且相同的条件下处理多个样本来进行多重分析。但是,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,样本位置可能会从设计网格中高度变形。因此,当样本标识在设计与其制造的输出之间不匹配时,这些失真可能会导致(组织学)分析结果的严重错误。开发了一种强大的方法来对TMA进行解阵列,该方法可以对TMA样本进行定位并与设计网格进行匹配,因此,克服这一突出技术的瓶颈至关重要。在本文中,我们提出了一种自动,快速且鲁棒的TMA去阵列(ATMAD)方法,该方法专门用于通过明场和荧光显微镜(或扫描仪)采集的图像。首先,通过应用局部自适应将组织样本定位在大图像中对输入TMA图像的各向同性小波变换进行阈值化。为了减少错误检测,考虑使用参数形状模型对每个检测到的位置处的椭圆形对象进行分割,将不符合尺寸和圆度标准的分割对象从组织样本列表中丢弃,然后再与设计网格进行匹配。通过估计薄板模型下的TMA网格变形来执行样品匹配。最后,由于估计的变形,通过运行第二分割步骤,可以识别出在早期图像处理步骤中被初步剔除的真实组织样本。我们为TMA分析开发了一种新颖的解排列方法。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高动态,信噪比差,背景复杂和TMA网格非线性变形的TMA图像。此外,变形估计会产生定量信息,以保证TMA的制造质量。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号