Enhancing the quality metric of protein microarray image

The novel method of improving the quality metric of protein microarray image presented in this paper reduces impulse noise by using an adaptive median filter that employs the switching scheme based on local statistics characters; and achieves the impulse detection by using the difference between the standard deviation of the pixels within the filter window and the current pixel of concern. It also uses a top-hat filter to correct the background variation. In order to decrease time consumption, the top-hat filter core is cross structure. The experimental results showed that, for a protein microarray image contaminated by impulse noise and with slow background variation, the new method can significantly increase the signal-to-noise ratio, correct the trends in the background, and enhance the flatness of the background and the consistency of the signal intensity.     

CT影像组学模型对食管癌术后个体化辅助治疗的评估价值

目的:构建及验证基于影像组学预测食管鳞状细胞癌切除术后的无病生存期(DFS)模型，并进一步其评估术后辅助治疗的价值。方法:回顾性分析184例接受手术治疗的食管鳞状细胞癌患者。放射科医师手工勾画患者治疗前CT图像上的肿瘤感兴趣区后提取影像组学特征，采用最小绝对收缩和选择算子(LASSO)-Cox回归筛选关键特征并构建影像组学模型。同时，通过临床特征筛选及建立临床模型，结合以上模型后得到影像组学-临床模型。采用一致性指数(C指数)评价模型的预测效能。结果:影像组学模型由10个影像组学特征构成。病理淋巴结分期为DFS独立的临床风险因素。影像组学-临床模型表现出对肿瘤进展高低风险人群的良好区分度，训练集C指数为0.744,95%置信区间(CI)为0.689～0.799,验证集C指数为0.774,95%CI为0.676～0.872。高风险组中位DFS明显小于低风险组其中训练集(16个月vs 39个月，P<0.001);验证集(11个月vs 24个月，P<0.001)。同时，在训练集的低风险患者中，手术后辅助治疗患者与单纯手术患者的DFS比较无显著统计学差异(DFS分别为34.4个月，...