Rabinovich Andrew, Krajewski Stan, Krajewska Maryla, Shabaik Ahmed, Hewitt Stephen M, Belongie Serge, Reed John C, Price Jeffrey H
Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA.
IEEE Trans Inf Technol Biomed. 2006 Apr;10(2):209-19. doi: 10.1109/titb.2005.855544.
Increasingly automated techniques for arraying, immunostaining, and imaging tissue sections led us to design software for convenient management, display, and scoring. Demand for molecular marker data derived in situ from tissue has driven histology informatics automation to the point where one can envision the computer, rather than the microscope, as the primary viewing platform for histopathological scoring and diagnoses. Tissue microarrays (TMAs), with hundreds or even thousands of patients' tissue sections on each slide, were the first step in this wave of automation. Via TMAs, increasingly rapid identification of the molecular patterns of cancer that define distinct clinical outcome groups among patients has become possible. TMAs have moved the bottleneck of acquiring molecular pattern information away from sampling and processing the tissues to the tasks of scoring and results analyses. The need to read large numbers of new slides, primarily for research purposes, is driving continuing advances in commercially available automated microscopy instruments that already do or soon will automatically image hundreds of slides per day. We reviewed strategies for acquiring, collating, and storing histological images with the goal of streamlining subsequent data analyses. As a result of this work, we report an implementation of software for automated preprocessing, organization, storage, and display of high resolution composite TMA images.
用于组织切片排列、免疫染色和成像的自动化技术日益增多，促使我们设计出便于管理、显示和评分的软件。对源自组织原位的分子标记数据的需求推动了组织学信息学自动化的发展，以至于人们可以设想将计算机而非显微镜作为组织病理学评分和诊断的主要观察平台。组织微阵列（TMA），即每张载玻片上有数百甚至数千个患者的组织切片，是这一波自动化浪潮的第一步。通过TMA，越来越快速地识别癌症的分子模式从而在患者中定义不同临床结局组已成为可能。TMA已将获取分子模式信息的瓶颈从组织采样和处理转移到评分和结果分析任务上。主要出于研究目的而读取大量新载玻片的需求，正推动着市售自动化显微镜仪器不断取得进展，这些仪器已经或很快就能每天自动对数百张载玻片进行成像。我们回顾了获取、整理和存储组织学图像的策略，目的是简化后续的数据分析。这项工作的成果是，我们报告了一款用于自动预处理、组织、存储和显示高分辨率复合TMA图像的软件的实现。
