Identification of HEp-2 specimen images with mitotic cell patterns

In this paper, we propose and analyze a novel framework to identify the HEp-2 specimen images, consisting of mitotic spindle (MS) pattern cells. It is based on the fact that the cells showing MS patterns will always be present with other interphase type pattern cells, in a whole slide image (WSI) or specimen image, but the number of MS cells will be very small, unlike interphase patterns. Considering this fact, the work contributes in presenting a framework, using different strategies such as cells-based, region-based and complete image-based approaches. For cell-based approach, the distinctive characteristic of MS patterns is represented using morphology and texture-based features, followed by a traditional Support Vector Machine (SVM) based classifier, whereas the region-based approach uses a Convolutional Neural Network (CNN) as feature extractor and baseline classifier. Finally, for the image-based approach, the Faster Region-CNN (Faster-RCNN) based object detection framework has been applied, considering MS patterns as distinct objects. The region and image-based approaches also contribute in avoiding the requirement of DAPI based segmentation masks. Another contribution of this work is to use a novel and clearly specified threshold-based decision-making criteria for patterns declaration of the specimens with MS pattern cells. All the proposed strategies, integrated with decision-making criteria are validated on a publicly available dataset and across various experiments, we demonstrate good performance, i.e., max. MCC 0.92 in one case. Hence, the proposed framework proves to be an effective solution for the problem statement. (C) 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.