Evaluation of three algorithms for the segmentation of overlapping cervical cells

Lu, Zhi, Carneiro, Gustavo, Bradley, Andrew P. , Ushizima, Daniela, Nosrati, Masoud S. , Bianchi, Andrea G. C. , Carneiro, Claudia M. and Hamarneh, Ghassan (2017) Evaluation of three algorithms for the segmentation of overlapping cervical cells. IEEE Journal of Biomedical and Health Informatics, 21 2: 441-450. doi:10.1109/JBHI.2016.2519686


Author Lu, Zhi
Carneiro, Gustavo
Bradley, Andrew P.
Ushizima, Daniela
Nosrati, Masoud S.
Bianchi, Andrea G. C.
Carneiro, Claudia M.
Hamarneh, Ghassan
Title Evaluation of three algorithms for the segmentation of overlapping cervical cells
Journal name IEEE Journal of Biomedical and Health Informatics   Check publisher's open access policy
ISSN 2168-2194
2168-2208
Publication date 2017-03-01
Sub-type Article (original research)
DOI 10.1109/JBHI.2016.2519686
Open Access Status Not yet assessed
Volume 21
Issue 2
Start page 441
End page 450
Total pages 10
Place of publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Collection year 2018
Language eng
Abstract In this paper, we introduce and evaluate the systems submitted to the first Overlapping Cervical Cytology Image Segmentation Challenge, held in conjunction with the IEEE International Symposium on Biomedical Imaging 2014. This challenge was organized to encourage the development and benchmarking of techniques capable of segmenting individual cells from overlapping cellular clumps in cervical cytology images, which is a prerequisite for the development of the next generation of computer-aided diagnosis systems for cervical cancer. In particular, these automated systems must detect and accurately segment both the nucleus and cytoplasm of each cell, even when they are clumped together and, hence, partially occluded. However, this is an unsolved problem due to the poor contrast of cytoplasm boundaries, the large variation in size and shape of cells, and the presence of debris and the large degree of cellular overlap. The challenge initially utilized a database of 16 high-resolution (times40 magnification) images of complex cellular fields of view, in which the isolated real cells were used to construct a database of 945 cervical cytology images synthesized with a varying number of cells and degree of overlap, in order to provide full access of the segmentation ground truth. These synthetic images were used to provide a reliable and comprehensive framework for quantitative evaluation on this segmentation problem. Results from the submitted methods demonstrate that all the methods are effective in the segmentation of clumps containing at most three cells, with overlap coefficients up to 0.3. This highlights the intrinsic difficulty of this challenge and provides motivation for significant future improvement.
Keyword Challenge
Overlapping cell segmentation
Pap smear image analysis
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
 
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