Sack Ulrich, Knoechner Stephan, Warschkau Holger, Pigla Ullrich, Emmrich Frank, Kamprad Manja
Institute of Clinical Immunology and Transfusion Medicine, University of Leipzig, Johannisallee 30, 04103 Leipzig, Germany.
Autoimmun Rev. 2003 Sep;2(5):298-304. doi: 10.1016/s1568-9972(03)00067-3.
Indirect immunofluorescence with HEp-2 cells presents the major screening method for detection of autoantibodies in systemic autoimmune diseases. Hereby, a large variety of autoantibody entities can be detected and recognized by at least partially typic fluorescence patterns. Currently, this method requires highly specialized technicians and resists automatization. Nevertheless, requirements of good laboratory practice, especially standardization and documentation are hampered by the common microscopic technique. Here, we present a computer-assisted system for classification of interphase HEp-2 immunofluorescence patterns in autoimmune diagnostics. Designed as an assisting system, representative patterns are acquired by an operator with a digital microscope camera and transferred to a personal computer. By use of a novel software package based on image analysis, feature extraction and machine learning algorithms, relevant characteristics describing patterns could be found out. Our results show that identification of positive fluorescence and pre-differentiation between most important HEp-2 staining patterns can be performed by this system. Results and documentation of fluorescence patterns can be integrated into the laboratory system. To enable the usage of such a system in routine diagnostics, accuracy of this system and correct recognition of interferring patterns must be further improved.
以人喉上皮癌细胞（HEp-2细胞）进行间接免疫荧光检测是系统性自身免疫性疾病中自身抗体检测的主要筛查方法。据此，通过至少部分典型的荧光模式可检测和识别多种自身抗体实体。目前，该方法需要高度专业的技术人员，且难以实现自动化。然而，常规显微镜技术阻碍了良好实验室规范的要求，尤其是标准化和记录工作。在此，我们展示了一种用于自身免疫诊断中对间期HEp-2免疫荧光模式进行分类的计算机辅助系统。作为辅助系统，操作人员使用数码显微镜相机获取代表性模式并传输至个人计算机。通过使用基于图像分析、特征提取和机器学习算法的新型软件包，可找出描述模式的相关特征。我们的结果表明，该系统能够识别阳性荧光并对最重要的HEp-2染色模式进行预区分。荧光模式的结果和记录可整合到实验室系统中。为使此类系统能够用于常规诊断，必须进一步提高该系统的准确性以及对干扰模式的正确识别能力。
