Background Cervical cancer remains a significant health concern worldwide, necessitating effective diagnostic methods such as cervical cell image segmentation. This review outlines the challenges and importance of accurately segmenting cervical cell images in medical diagnostics. Objective This study explores the application of deep learning techniques in cervical cell image segmentation, focusing on convolutional neural networks (CNNs), fully convolutional networks, non-coding RNAs and U-Net models. It aims to compare their characteristics, strengths, and weaknesses in enhancing segmentation precision. Methods The article surveys recent advancements in deep learning-based cervical cell image segmentation, drawing insights from English literature. It highlights how CNN architectures excel in feature extraction and precise image segmentation, particularly in the context of cervical cells. Results Deep learning methodologies, particularly CNN-based models, have significantly improved the accuracy and efficiency of cervical cell image segmentation. Researchers have increasingly adopted these techniques to refine diagnostic capabilities. Conclusion The evolving landscape of cervical cell image segmentation, propelled by deep learning advancements, promises enhanced precision and efficiency in clinical diagnostics and treatment support. Future research should continue exploring these technologies to further improve medical outcomes.

Investigation of Cervical Cell Image Segmentation Technology Based on Deep Learning and Non-coding RNAs

The segmentation of cervical cells is the core part of the automatic analysis system of cervical smear. In this paper, we propose a method for segmentation of EDF images of overlapping cervical cells based on similarity measure. Based on the consistency of focus and position information between cytoplasm and target nucleus, the similarity measure of cytoplasm is constructed by using the characteristic of EDF image which is composed of several images with different focal points. Based on similarity measure, the cytoplasmic contour was tracked to achieve rough segmentation. Finally, regularized level set evolution (DRLSE) was used to refine the obtained contour at the pixel level. The approach was evaluated based on the public dataset-ISBI2015 which is superior to existing methods in image segmentation of EDF for overlapping cells.

A Framework for Cervical Cell EDF Image Segmentation Using Similarity Measure

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A Framework for Cervical Cell EDF Image Segmentation Using Similarity Measure