Computer-assisted classification of HEp-2 immunofluorescence patterns in autoimmune diagnostics

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.