HEp-2 Specimen Cell Detection and Classification Using Very Deep Convolutional Neural Networks-Based Cell Shape

The accurate detection and classification of HEp-2 specimen staining plays a key role in autoimmune disease diagnosis and transplantation assessment. Such detection and classification is challenging due to the abundant presence of highly overlapped cells, variations in cell densities, the variety of staining patterns, large numbers of cells per image, large data volumes and overfitting of features. In this paper, a robust technique is proposed to deal with images of all staining patterns and address these challenges. Very deep convolutional neural networks with a layer structure inspired by the standard architecture of the VGG-16 image is proposed for classification of HEp-2 staining cells based on cell shape and adapted to consider overfitting. Level set method using geometric active contours with morphological opening and Delaunay triangulation is used for cell segmentation and splitting. The cell segmentation method also considers overlapped cells. The proposed method has been tested and compared with other methods using Task-2 training dataset from competitions held on the ICPR2014 and ICPR2016. A extensive study demonstrates that the proposed method outperforms all other methods and promises to support the diagnosis of autoimmune diseases and allograft rejection prediction in future pathology practice, except for one method in this study which is slightly better than our proposed method.