A Deep Learning approach for automated Cytoplasm and Nuclei cervical segmentation

Abstract

Cervical cancer is a severe disease that affects many women in developing countries. Increasing screening capabilities is the best tool to reduce cancer incidence and save lives. Segmentation is an essential task in screening because it can lead to a better understanding of the morphological characteristics of cells. This paper presents a method for multi-class cell segmentation into Nuclei and Cytoplasm regions. Using the Herlev dataset for evaluation, this work achieved good performance by using state-of-the-art classification architectures such as EfficientNet combined with Feature Pyramid Networks to complete the segmentation task. Performance metrics for both classes show that the approach is robust enough to complete the task. The model achieved a 0.91 F1 score, 0.85 IoU, 0.91 Precision, 0.92 Recall, and 0.96 Specificity as class average with a very low standard deviation, validated with a 5-fold cross-validation. The proposal can help experts correctly asset cervical cell lesions and provide better healthcare for the patients.