一种用于组织病理学标本的基于caGRID和学习的图像分割方法。
A caGRID-ENABLED, LEARNING BASED IMAGE SEGMENTATION METHOD FOR HISTOPATHOLOGY SPECIMENS.
作者信息
Foran David J, Yang Lin, Tuzel Oncel, Chen Wenjin, Hu Jun, Kurc Tahsin M, Ferreira Renato, Saltz Joel H
机构信息
The Cancer Institute of New Jersey, UMDNJ-RWJMS, Piscataway, NJ 08854.
出版信息
Proc IEEE Int Symp Biomed Imaging. 2009 Jul 1;6:1306-1309. doi: 10.1109/ISBI.2009.5193304.
Accurate segmentation of tissue microarrays is a challenging topic because of some of the similarities exhibited by normal tissue and tumor regions. Processing speed is another consideration when dealing with imaged tissue microarrays as each microscopic slide may contain hundreds of digitized tissue discs. In this paper, a fast and accurate image segmentation algorithm is presented. Both a whole disc delineation algorithm and a learning based tumor region segmentation approach which utilizes multiple scale texton histograms are introduced. The algorithm is completely automatic and computationally efficient. The mean pixel-wise segmentation accuracy is about 90%. It requires about 1 second for whole disc (1024×1024 pixels) segmentation and less than 5 seconds for segmenting tumor regions. In order to enable remote access to the algorithm and collaborative studies, an analytical service is implemented using the caGrid infrastructure. This service wraps the algorithm and provides interfaces for remote clients to submit images for analysis and retrieve analysis results.
由于正常组织和肿瘤区域存在一些相似性，组织微阵列的精确分割是一个具有挑战性的课题。在处理成像的组织微阵列时，处理速度是另一个需要考虑的因素，因为每张显微镜载玻片可能包含数百个数字化的组织切片。本文提出了一种快速准确的图像分割算法。介绍了一种全切片勾勒算法和一种基于学习的肿瘤区域分割方法，该方法利用了多尺度纹理直方图。该算法完全自动化且计算效率高。逐像素分割的平均准确率约为90%。全切片（1024×1024像素）分割大约需要1秒，肿瘤区域分割不到5秒。为了实现对该算法的远程访问和协作研究，使用caGrid基础设施实现了一种分析服务。该服务封装了算法，并为远程客户端提供接口，以便提交图像进行分析并检索分析结果。
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