Abstract
Cervical cancer is the one of the most common cancers in women worldwide, affecting around 570,000 new patients each year. Although there have been great improvements over the years, current screening procedures can still suffer from long and tedious workflows and ambiguities. The increasing interest in the development of computer-aided solutions for cervical cancer screening is to aid with these common practical difficulties, which are especially frequent in the low-income countries where most deaths caused by cervical cancer occur. In this review, an overview of the disease and its current screening procedures is firstly introduced. Furthermore, an in-depth analysis of the most relevant computational methods available on the literature for cervical cells analysis is presented. Particularly, this work focuses on topics related to automated quality assessment, segmentation and classification, including an extensive literature review and respective critical discussion. Since the major goal of this timely review is to support the development of new automated tools that can facilitate cervical screening procedures, this work also provides some considerations regarding the next generation of computer-aided diagnosis systems and future research directions.
Highlights
Cervical cancer is the fourth most common cancer in women worldwide, and the second most frequent in low-income countries [1]
Since the in-depth understanding of the domain-specific knowledge gained by human experts on the problem being addressed can be of extreme importance for the design of a reliable and effective feature extraction engine [139], we present a set of popular image features already in use to characterize cervical cells
This review offers a contextualization of the current cervical cancer screening procedures, as well as an in-depth analysis of the most relevant computational methods available on the literature for cervical cells analysis
Summary
Cervical cancer is the fourth most common cancer in women worldwide, and the second most frequent in low-income countries [1]. There are an estimated 570,000 new cases and 311,000 deaths from cervical cancer each year, 85% of them occurring in low and middle-income countries [2]. The increasing interest in the development of computer-aided diagnosis (CADx) systems for cervical cancer screening is closely related with the common practical difficulties experienced in these under-resourced health facilities, such as the shortage of specialized staff and equipment. Computer vision and machine learning approaches are often used in CADx systems to reduce the dependence of manual microscopic examination of cervical cytology smears, which is an exhaustive and time consuming activity, simultaneously requiring a considerable expertise of the cytotechnologist. There is a wide rage of computer vision tasks that are highly relevant for this application area, such as: automated handling of smears variability; detection of artifacts; segmentation of individual cells and cell clusters; segmentation of nuclei and cytoplasm for each individual cell; and automated detection of abnormal changes in cell morphology
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