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Centromere and cytoplasmic staining pattern recognition: a local approach

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Abstract

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 %.

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Acknowledgments

The authors wish to thank Prof. A. Afeltra, Dr. A. Rigon and Dr. F. Buzzulini for their help in collecting and annotating the images. This work has been carried out in the framework of the ITINERIS2 project, Codice CUP F87G10000080009, under the financial support of Regione Lazio (Programme “Sviluppo dell’Innovazione Tecnologica nel Territorio Regionale”, Art. 182, comma 4, lettera c), L.R. no. 4, 28 Aprile 2006).

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  1. Computer Science and Bioinformatics Laboratory, Integrated Research Centre, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128, Rome, Italy

    Giulio Iannello, Leonardo Onofri & Paolo Soda

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  1. Giulio Iannello
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  2. Leonardo Onofri
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  3. Paolo Soda
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Correspondence to Leonardo Onofri.

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Iannello, G., Onofri, L. & Soda, P. Centromere and cytoplasmic staining pattern recognition: a local approach. Med Biol Eng Comput 51, 1305–1314 (2013). https://doi.org/10.1007/s11517-013-1102-1

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