A Review of Computational Methods for Cervical Cells Segmentation and Abnormality Classification

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.

Keywords: cervical cancer; classification; computer-aided diagnosis; machine learning; pap-smear; screening; segmentation.

Figure 1

Satisfactory (a) and unsatisfactory (b) LBC preparations. From: Nayar, R.; Wilbur, D. The Bethesda System for Reporting Cervical Cytology: Definitions, Criteria, and Explanatory Notes, 3rd ed.; Springer International Publishing, 2015 [26] and reproduced with permission of Springer.

Figure 2

Atypical squamous cells on liquid-based cytology (LBC). From: Nayar, R.; Wilbur, D. The Bethesda System for Reporting Cervical Cytology: Definitions, Criteria, and Explanatory Notes, 3rd ed.; Springer International Publishing, 2015 [26] and reproduced with permission of Springer.

Figure 3

Atypical glandular cells on LBC. From: Nayar, R.; Wilbur, D. The Bethesda System for Reporting Cervical Cytology: Definitions, Criteria, and Explanatory Notes, 3rd ed.; Springer International Publishing, 2015 [26] and reproduced with permission of Springer.

Figure 4

Sample images and corresponding classification of Herlev and CERVIX93 datasets.

Figure 5

Sample images and corresponding segmentation masks of ISBI14 and ISBI15 datasets.