Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels

RonaldJoe Stanley, Sudhir Sornapudi, WilliamV Stoecker, Haidar Almubarak, Rodney Long, Sameer Antani, George Thoma, Rosemary Zuna, ShellianeR Frazier
2018 Journal of Pathology Informatics  
Original Article IntroductIon The reconstruction of medical images into digital form has propelled the fields of medical research and clinical practice. [1] Image processing for histopathology image applications still has numerous challenges to overcome, especially in accurate nuclei detection. Cervical cancer is the fourth most prevalent female cancer globally. [2] Over 500,000 new cases of this cancer are reported annually, especially in Africa; over half of this total eventuates in death.
more » ... There is a cure for cervical cancer if it is detected early. The gold standard for early cervical cancer diagnosis is the microscopic evaluation of histopathology images by a qualified pathologist. [3] [4] [5] [6] The severity of cervical cancer increases as the immature atypical cells in the epithelium region increase. Based on this observation, cancer affecting squamous epithelium is classified as normal or one of the three increasingly premalignant grades of cervical intraepithelial neoplasia (CIN): CIN1, CIN2, and CIN3 [4] [5] [6] [ Figure 1 ]. Normal means there is no CIN; CIN1 corresponds to mild dysplasia (abnormal change); CIN2 denotes moderate dysplasia; and CIN3 corresponds to severe dysplasia. With increasing CIN grade, the epithelium has been observed to show delayed maturation with an increase in immature atypical cells from bottom (basal membrane) to top of the epithelium region. [6] [7] [8] [9] [10] This can be observed from Figure 1 . Atypical immature cells are most dense in the bottom region of the epithelium for CIN1 [ Figure 1b ]. For CIN2, two-thirds of the bottom region is affected by the atypical immature Background: Advances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images. Detection of nuclei is critical for squamous epithelium cervical intraepithelial neoplasia (CIN) classification into normal, CIN1, CIN2, and CIN3 grades. Methods: In this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network. Results: The proposed approach was evaluated on a dataset of 133 digitized histology images and achieved an overall nuclei detection (object-based) accuracy of 95.97%, with demonstrated improvement over imaging-based and clustering-based benchmark techniques. Conclusions: The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods.
doi:10.4103/jpi.jpi_74_17 pmid:29619277 pmcid:PMC5869967 fatcat:z64odumz4fc2rj7zigvx5wgxna