Joint Intensity Classification and Specimen Segmentation on HEp-2 Images: a Deep Learning Approach

Antinuclear antibody (ANA) testing is performed to help diagnose patients with possible autoimmune diseases. The indirect immunofluorescence (IIF) microscopy performed with human epithelial type 2 (HEp-2) cells as the substrate is the reference method for ANA screening. It allows for the detection of antibody binding to specific intracellular targets, resulting in distinct staining patterns whose recognition may be helpful for diagnosis purposes. In recent years, there has been an increasing interest in devising deep learning methods for automatic segmentation and classification of staining patterns, as well as for other tasks related to this diagnostic technique. However, little attention has been paid to architectures aimed at managing more functions related to ANA testing by simultaneously training tasks that use a shared representation. In this paper, we propose a deep neural network model based on U-Net that exploits an end-to-end approach for joint intensity classification and specimen segmentation on HEp-2 cell images. To the best of our knowledge, this is the first work proposing the adoption of a single framework tailored to address these two mandatory tasks in the HEp-2 clinical workflow. The experiments were conducted on I3A, the largest publicly available dataset of HEp-2 images. The results showed that the proposed approach outperformed the competing state-of-the-art methods for both the considered tasks, achieving (in 5-fold cross validation) 93.83% and 90.21% for intensity classification accuracy and segmentation accuracy, respectively.