A whole-slide image grading benchmark and tissue classification for cervical cancer precursor lesions with inter-observer variability

Abdulkadir Albayrak, Asli Unlu Akhan, Nurullah Calik, Abdulkerim Capar, Gokhan Bilgin*, Behcet Ugur Toreyin, Bahar Muezzinoglu, Ilknur Turkmen, Lutfiye Durak-Ata

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: Dergiye katkıMakalebilirkişi

16 Atıf (Scopus)

Özet

The cervical cancer developing from the precancerous lesions caused by the human papillomavirus (HPV) has been one of the preventable cancers with the help of periodic screening. Cervical intraepithelial neoplasia (CIN) and squamous intraepithelial lesion (SIL) are two types of grading conventions widely accepted by pathologists. On the other hand, inter-observer variability is an important issue for final diagnosis. In this paper, a whole-slide image grading benchmark for cervical cancer precursor lesions is created and the “Uterine Cervical Cancer Database” introduced in this article is the first publicly available cervical tissue microscopy image dataset. In addition, a morphological feature representing the angle between the basal membrane (BM) and the major axis of each nucleus in the tissue is proposed. The presence of papillae of the cervical epithelium and overlapping cell problems are also discussed. Besides that, the inter-observer variability is also evaluated by thorough comparisons among decisions of pathologists, as well as the final diagnosis. [Figure not available: see fulltext.].

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)1545-1561
Sayfa sayısı17
DergiMedical and Biological Engineering and Computing
Hacim59
Basın numarası7-8
DOI'lar
Yayın durumuYayınlandı - Ağu 2021

Bibliyografik not

Publisher Copyright:
© 2021, International Federation for Medical and Biological Engineering.

Finansman

The authors also would like to thank Argenit Company and Istanbul Medipol University Hospital for providing and annotating the whole-slide histopathological images of cervical cancer precursor lesions image dataset. The authors would like to thank the reviewers for all useful and instructive comments on our manuscript. This work is in part funded by ITU BAP MAB-2020-42314 project and also supported by the Scientific Research Projects Coordination Department, Yildiz Technical University, under Project 2014-04-01-KAP01.

FinansörlerFinansör numarası
Argenit Company
Istanbul Medipol University Hospital
International Technological UniversityBAP MAB-2020-42314
Yildiz Teknik Üniversitesi2014-04-01-KAP01

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