Computer Science and Bioinformatics Laboratory, Integrated Research Centre, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128, Rome, Italy,
Med Biol Eng Comput. 2013 Dec;51(12):1305-14. doi: 10.1007/s11517-013-1102-1. Epub 2013 Jul 23.
Autoimmune diseases are very serious and also invalidating illnesses. The benchmark procedure for their diagnosis is the indirect immunofluorescence (IIF) assay performed on the HEp-2 substrate. Medical doctors first determine the fluorescence intensity exhibited by HEp-2 wells and then report the staining pattern. Despite its pivotal role, IIF is affected by inter- and intra-laboratory variabilities demanding for the development of computer-aided-diagnosis tools supporting medical doctor decisions. With reference to staining pattern recognition, state-of-the-art approaches recognize five main patterns characterized by well-defined cell edges. These approaches are based on cell segmentation, a task that recent work suggests to be harder than the classification itself. In this paper, we extend the panel of detectable HEp-2 staining patterns, introducing the recognition of centromere and cytoplasmic patterns, which have a high specific match with certain autoimmune diseases, from other stainings. Since image segmentation algorithms fail on these samples, we developed a classification system integrating local descriptors and the bag of visual word approach, which represents image contents without the burden of segmentation. We tested our approach on a large dataset of HEp-2 images with high variability in both fluorescence intensity and staining patterns correctly recognizing the 97.12 % of samples. The system has also been validated in a daily routine fashion on 108 consecutive IIF analyses of hospital outpatients and inpatients, achieving an accuracy rate of 97.22 %.
自身免疫性疾病非常严重，且会使人丧失劳动能力。此类疾病的诊断基准程序是在 HEp-2 基质上进行间接免疫荧光（IIF）检测。医生首先确定 HEp-2 孔的荧光强度，然后报告染色模式。尽管 IIF 具有关键作用，但它受到实验室间和实验室内部变异性的影响，因此需要开发计算机辅助诊断工具来支持医生的决策。关于染色模式识别，最先进的方法可识别五种主要的模式，这些模式的特点是细胞边缘清晰。这些方法基于细胞分割，最近的研究表明，这一任务比分类本身更具挑战性。在本文中，我们扩展了可检测的 HEp-2 染色模式面板，引入了着丝粒和细胞质模式的识别，这些模式与某些自身免疫性疾病具有高度特异性匹配，与其他染色区分开来。由于图像分割算法无法应用于这些样本，我们开发了一种分类系统，该系统集成了局部描述符和视觉词汇袋方法，无需分割即可表示图像内容。我们在具有高度荧光强度和染色模式变异性的大量 HEp-2 图像数据集上测试了我们的方法，正确识别了 97.12%的样本。该系统还在 108 例连续的门诊和住院患者的 IIF 分析中进行了日常验证，准确率达到 97.22%。
