Doyle Scott, Madabhushi Anant, Feldman Michael, Tomaszeweski John
Dept. of Biomedical Engineering, Rutgers Univ., Piscataway, NJ 08854, USA.
Med Image Comput Comput Assist Interv. 2006;9(Pt 2):504-11. doi: 10.1007/11866763_62.
Current diagnosis of prostatic adenocarcinoma is done by manual analysis of biopsy tissue samples for tumor presence. However, the recent advent of whole slide digital scanners has made histopathological tissue specimens amenable to computer-aided diagnosis (CAD). In this paper, we present a CAD system to assist pathologists by automatically detecting prostate cancer from digitized images of prostate histological specimens. Automated diagnosis on very large high resolution images is done via a multi-resolution scheme similar to the manner in which a pathologist isolates regions of interest on a glass slide. Nearly 600 image texture features are extracted and used to perform pixel-wise Bayesian classification at each image scale to obtain corresponding likelihood scenes. Starting at the lowest scale, we apply the AdaBoost algorithm to combine the most discriminating features, and we analyze only pixels with a high combined probability of malignancy at subsequent higher scales. The system was evaluated on 22 studies by comparing the CAD result to a pathologist's manual segmentation of cancer (which served as ground truth) and found to have an overall accuracy of 88%. Our results show that (1) CAD detection sensitivity remains consistently high across image scales while CAD specificity increases with higher scales, (2) the method is robust to choice of training samples, and (3) the multi-scale cascaded approach results in significant savings in computational time.
目前前列腺腺癌的诊断是通过对活检组织样本进行人工分析以确定是否存在肿瘤。然而，全玻片数字扫描仪的出现使得组织病理学组织标本适用于计算机辅助诊断（CAD）。在本文中，我们提出了一种CAD系统，通过从前列腺组织学标本的数字化图像中自动检测前列腺癌来协助病理学家。通过类似于病理学家在载玻片上分离感兴趣区域的方式的多分辨率方案，对非常大的高分辨率图像进行自动诊断。提取近600个图像纹理特征，并用于在每个图像尺度上进行逐像素贝叶斯分类，以获得相应的似然场景。从最低尺度开始，我们应用AdaBoost算法来组合最具区分性的特征，并且在随后更高的尺度上仅分析具有高恶性综合概率的像素。通过将CAD结果与病理学家对癌症的手动分割（作为真实情况）进行比较，在22项研究中对该系统进行了评估，发现其总体准确率为88%。我们的结果表明：（1）CAD检测灵敏度在各个图像尺度上始终保持较高水平，而CAD特异性随着尺度的增加而提高；（2）该方法对训练样本的选择具有鲁棒性；（3）多尺度级联方法显著节省了计算时间。
