Semi-automated imaging system to quantitate estrogen and progesterone receptor immunoreactivity in human breast cancer

A semi‐automated imaging system is described to quantitate estrogen and progesterone receptor immunoreactivity in human breast cancer. The system works for any conventional method of image acquisition using microscopic slides that have been processed for immunohistochemical analysis of the estrogen receptor and progesterone receptor. Estrogen receptor and progesterone receptor immunohistochemical staining produce colorimetric differences in nuclear staining that conventionally have been interpreted manually by pathologists and expressed as percentage of positive tumoral nuclei. The estrogen receptor and progesterone receptor status of human breast cancer represent important prognostic and predictive markers of human breast cancer that dictate therapeutic decisions but their subjective interpretation result in interobserver, intraobserver and fatigue variability. Subjective measurements are traditionally limited to a determination of percentage of tumoral nuclei that show positive immunoreactivity. To address these limitations, imaging algorithms utilizing both colorimetric (RGB) as well as intensity (gray scale) determinations were used to analyze pixels of the acquired image. Image acquisition utilized either scanner or microscope with attached digital or analogue camera capable of producing images with a resolution of 20 pixels /10 μ. Areas of each image were screened and the area of interest richest in tumour cells manually selected for image processing. Images were processed initially by JPG conversion of SVS scanned virtual slides or direct JPG photomicrograph capture. Following image acquisition, images were screened for quality, enhanced and processed. The algorithm‐based values for estrogen receptor and progesterone receptor percentage nuclear positivity both strongly correlated with the subjective measurements (intraclass correlation: 0.77; 95% confidence interval: 0.59, 0.95) yet exhibited no interobserver, intraobserver or fatigue variability. In addition the algorithms provided measurements of nuclear estrogen receptor and progesterone receptor staining intensity (mean, mode and median staining intensity of positive staining nuclei), parameters that subjective review could not assess. Other semi‐automated image analysis systems have been used to measure estrogen receptor and progesterone receptor immunoreactivity but these either have required proprietary hardware or have been based on luminosity differences alone. By contrast our algorithms were independent of proprietary hardware and were based on not just luminosity and colour but also many other imaging features including epithelial pattern recognition and nuclear morphology. These features provide a more accurate, versatile and robust imaging analysis platform that can be fully automated in the near future. Because of all these properties, our semi‐automated imaging system ‘adds value’ as a means of measuring these important nuclear biomarkers of human breast cancer.     

多标号图像分割及其应用

介绍了一种基于多标号的半自动化图像分割方法。在分割过程中,首先依据高斯权值函数,针对待处理图像建立一个加权图;然后在原始图像中分别标记出属于不同目标区域的像素点;之后,任意选择图像中没有被标号的像素点为作起点,依据所创建的加权图进行随机游走,计算出从当前出发点游走至各个标记像素的概率。通过这种方法,针对图像中未被标号的像素,可以获得一个概率分布图,其中每个概率分布表示未标号像素随机游走到各个标记像素的概率,取概率最大的标记像素作为其所属目标,则可得到一个高质量的分割图像。     

一种半自动化安全数据交换模型

讨论了Internet环境下数据交换的特点，分析了一类在数据库间交换数据的应用所具有的特点，并在此基础上提出了一种独立于运行和开发平台的数据交换模型。该模型基于数据库间的直接连接，对应于一类广泛的数据交换应用。以一个可实际运行的原型系统为基础，详细讨论了该模型的体系结构、数据描述、数据交换通信协议(DECP)、安全性和半自动化等几方面的内容，并给出了实现指导。最后对进一步研究该模型给出了总结及展望。     

半自动婴儿摇篮床设计研究

通过对目前市场上常用的婴儿摇篮床产品的研究,找出其中的不足与危害,并对不足及其产生的危害进行了分析。根据大量的文献资料及市场调研,本着健康设计理念,从而设计研制出智能化、人性化、更科学的优质半自动婴儿摇篮床,并对各个装置进行了具体设计。     

高密度聚丙烯板半自动化机器人搅拌摩擦焊接参数优化

采用搅拌摩擦焊可进行热塑性材料的连接,通过搅拌头和工件间摩擦产生的焦耳热得以实现。相对于搅拌摩擦焊接的热塑性塑料,传统工具会在所焊接试样的背面产生一道焊缝,该焊缝会在根部形成缺陷,从而降低整个试件的抗拉强度。选用的高密度聚乙烯板具有高透明度,能够实时分析其在焊接过程中产生的形态变化。设置焊接参数为:探头自身旋转速度800~1 200 r/min,横向行进速度40~200 mm/min,轴向力1~5 k N。此外,还分别选用高度为5 mm的方形、柱形及具有一定角度的探头对试样进行焊接,最终通过检验焊缝处抗拉强度和观察微观结构来评估焊缝质量。     

半自动化多机照料作业车间多目标调度模型及算法

研究了半自动化作业车间生产情况,将半自动化作业车间生产时间按照过程分为自动化生产时间和生产准备时间,处于自动化生产时间的工人可同时照料多台设备,处于生产准备时间的工人无法同时照料其他设备。建立了一种包括设备资源和人力资源2种约束的半自动化多机照料的作业车间多目标优化调度模型,采用多机照料双层染色体编码方案、设定工人可照料多台设备、任一工人在任一时刻最多为1道工序进行生产准备的条件约束,基于双层染色体进行单点交叉变异,对模型求解并仿真分析,最后与半自动化单机照料作业车间调度情况对比。实验结果表明,半自动化多机照料作业车间调度能够缩短工期,降低成本。