Computer-aided Prognosis of Neuroblastoma on Whole-slide Images: Classification of Stromal Development.
Computer-aided Prognosis of Neuroblastoma on Whole-slide Images: Classification of Stromal Development.
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DOI：
10.1016/j.patcog.2008.08.027
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发表时间：
2009-06
影响因子：
8
通讯作者：
Gurcan, M. N.
中科院分区：
文献类型：
--
作者：
Sertel, O.;Kong, J.;Shimada, H.;Catalyurek, U. V.;Saltz, J. H.;Gurcan, M. N.
我们正在开发一个计算机辅助神经母细胞瘤（NB）的预后系统，神经系统癌症和影响儿童的最恶性肿瘤之一。组织学检查是NB临床常规诊断中制定治疗方案的重要环节。根据国际神经母细胞瘤病理学分类（Shimada系统），NB患者根据组织形态分为有利和不利的组织学。在这项研究中，我们提出了一个图像分析系统，操作数字化的H&E染色的全载玻片NB组织样本，并根据施旺氏间质发育的程度将每张载玻片分类为基质丰富或基质贫乏。我们的统计框架进行分类的基础上提取的纹理特征，使用共现统计和本地二进制模式。由于高分辨率的数字化全载玻片图像，我们提出了一种多分辨率的方法，模仿病理学家的评价，使图像分析从最低分辨率开始，并在必要时切换到更高的分辨率。我们采用了一个非线性特征选择步骤，在训练步骤中确定每个分辨率水平下最具鉴别力的特征。一个改进的k-最近邻分类器被用来确定分类的置信水平，以在特定的分辨率水平作出决定。所提出的方法进行了独立的测试，对43个全载玻片样本，并提供了88.4%的整体分类准确率。
We are developing a computer-aided prognosis system for neuroblastoma (NB), a cancer of the nervous system and one of the most malignant tumors affecting children. Histopathological examination is an important stage for further treatment planning in routine clinical diagnosis of NB. According to the International Neuroblastoma Pathology Classification (the Shimada system), NB patients are classified into favorable and unfavorable histology based on the tissue morphology. In this study, we propose an image analysis system that operates on digitized H&E stained whole-slide NB tissue samples and classifies each slide as either stroma-rich or stroma-poor based on the degree of Schwannian stromal development. Our statistical framework performs the classification based on texture features extracted using co-occurrence statistics and local binary patterns. Due to the high resolution of digitized whole-slide images, we propose a multi-resolution approach that mimics the evaluation of a pathologist such that the image analysis starts from the lowest resolution and switches to higher resolutions when necessary. We employ an offine feature selection step, which determines the most discriminative features at each resolution level during the training step. A modified k-nearest neighbor classifier is used to determine the confidence level of the classification to make the decision at a particular resolution level. The proposed approach was independently tested on 43 whole-slide samples and provided an overall classification accuracy of 88.4%.
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DOI：
10.1109/34.574797
发表时间：
1997-02-01
影响因子：
23.6
作者：
Jain, A;Zongker, D
通讯作者：
Zongker, D
影响因子：
4.6
作者：
Jafari-Khouzani, K;Soltanian-Zadeh, H
通讯作者：
Soltanian-Zadeh, H
影响因子：
10.6
作者：
Tabesh, Ali;Teverovskiy, Mikhail;Saidi, Olivier
通讯作者：
Saidi, Olivier
DOI：
10.1109/tsmc.1973.4309314
发表时间：
1973-01-01
期刊：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS
影响因子：
--
作者：
HARALICK, RM;SHANMUGAM, K;DINSTEIN, I
通讯作者：
DINSTEIN, I
影响因子：
3.8
作者：
Gurcan, MN;Sahiner, B;Hadjiiski, L
通讯作者：
Hadjiiski, L