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您的检索式：作者名="MASKEY Ninu"
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| 1 | Segmentation of Hematoxylin-Eosin stained breast cancer histopathological images based on pixel-wise SVM classifier显示文摘Hematoxylin-Eosin(HE) staining is the routine diagnostic method for breast cancer(BC), and large amounts of HE stained histopathological images are available for analysis. It is emergent to develop computational methods to efficiently and objectively analyze these images, with the aim of providing potentially better diagnostic and prognostic information for BC. This work focuses on analyzing our in-house HE stained histopathological images of breast cancer tissues. Since tumor nests(TNs) and stroma morphological characteristics can reflect the biological behaviors of breast invasive ductal carcinoma(IDC), accurate segmentation of TNs and the stroma is the first step towards the subsequent quantitative analysis. We first propose a method based on the pixel-wise support vector machine(SVM) classifier for segmenting TNs and the stroma, then extract four morphological characters related to the TNs from the images and investigate their relationships with the patients' 8-year disease free survival(8-DFS). The evaluation result shows that the classification based segmentation method is able to distinguish between TNs and stroma with 87.1% accuracy and 80.2% precision,suggesting that the proposed method is promising in segmenting HE stained IDC histopathological images. The Kaplan-Meier survival curves show that three morphological characters(number of TNs, total perimeter, and average area of TNs) in the images have statistical correlations with 8-DFS of the patients, illustrating that the segmented images can help to identify new morphological factors in IDC TNs for the prediction of BC prognosis. | QU AiPing CHEN JiaMei WANG LinWei YUAN JingPing YANG Fang XIANG QingMing MASKEY Ninu YANG GuiFang LIU Juan LI Yan | 2015 | Science China(Information Sciences)2015，58，9： | 12 |
| 2 | 计算机图像分析挖掘乳腺癌病理预后新指标显示文摘应用计算机辅助图像分析方法研究了230例乳腺浸润性导管癌(invasive ductal carcinoma of the breast,IDC)的1 150张苏木素-伊红组织病理图像。用支持向量机模型分割上皮-间质,用标记点控制的分水岭算法分割细胞核,提取出730个形态学特征。Kaplan-Meier生存分析显示12个形态学特征与8年无病生存相关(P<0.05);Cox比例风险回归模型显示其中4个参数为独立预后因子:癌巢细胞密度(HR 1.645,95%CI[1.193,2.270],P=0.002)、间质细胞结构特征(HR 1.507,95%CI[1.084,2.095],P=0.015)、癌巢特征(HR 1.361,95%CI[1.026,1.804],P=0.032)及癌巢细胞核特征(HR 0.731,95%CI[0.538,0.993],P=0.045),可作为预测IDC预后的病理新指标。 | 陈佳梅 屈爱平 王林伟 袁静萍 杨芳 向清明 Ninu Maskey 杨桂芳 刘娟 李雁 | 2014 | 生物物理学报2014，0，7： | 2 |
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