Cell detection on digitized Pap smear images using ensemble of conventional image processing and deep learning techniques

• Published in: 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA) pp. 38 - 42
• Main Authors: Harangi, Balazs, Toth, Janos, Bogacsovics, Gergo, Kupas, David, Kovacs, Laszlo, Hajdu, Andras
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2019
• Subjects: cell segmentation; Computer architecture; Deep learning; Image segmentation; Microprocessors; Pap smear test; region-based combination; Sensitivity; Signal processing algorithms; Training
• ISSN: 1849-2266
• DOI: 10.1109/ISPA.2019.8868683

In this paper, we focus on the problem of cell segmentation in digitized Pap smear images, which is a prerequisite of automatically detecting cervical cancer in its early stage. According to the trends, we consider deep learning based approaches in the form of applying fully convolutional neural networks (FCNNs). A common bottleneck of deep learning is that large annotated dataset is required for proper training. As large public datasets are not yet available in this field, we have composed a corresponding manually labeled dataset. Though this dataset is quite large, the manual annotation is less reliable in this domain, so we had to apply such a deep learning framework that is able to overcome this issue. Accordingly, we have applied such an ensemble of FCNN and traditional segmentation approaches that provide sufficiently large diversity according to the most challenging manual annotation-related issues, like the inaccurate selection of cell boundaries. We propose ensembles to merge the outputs of the different segmentation methods, which have been proven superior to any of the ensemble members according to our experimental studies.