title: Evaluation of Three Algorithms for the Segmentation of Overlapping Cervical Cells.
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 ( ×40 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.
authors.affiliation: Australian Centre for Visual Technologies, The University of Adelaide, Adelaide, S.A., Australia.
authors.forename: Zhi
authors.initials: Z
authors.lastname: Lu
authors.affiliation: Australian Centre for Visual Technologies, The University of Adelaide, Adelaide, S.A., Australia.
authors.forename: Gustavo
authors.initials: G
authors.lastname: Carneiro
authors.affiliation: School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Qld., Australia.
authors.forename: Andrew P
authors.initials: AP
authors.lastname: Bradley
authors.affiliation: Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley Institute for Data Science, University of California, Berkeley, CA, USA.
authors.forename: Daniela
authors.initials: D
authors.lastname: Ushizima
authors.affiliation: Medical Image Analysis Lab, Simon Fraser University, Burnaby, BC, Canada.
authors.forename: Masoud S
authors.initials: MS
authors.lastname: Nosrati
authors.affiliation: Computer Science Department, Federal University of Ouro Preto, Ouro Preto, MG CEP, Brazil.
authors.forename: Andrea G C
authors.initials: AGC
authors.lastname: Bianchi
authors.affiliation: Cytology Laboratory, Clinical Analysis Department, Pharmacy School, Federal University of Ouro Preto, Ouro Preto, MG CEP, Brazil.
authors.forename: Claudia M
authors.initials: CM
authors.lastname: Carneiro
authors.affiliation: Medical Image Analysis Lab, Simon Fraser University, Burnaby, BC, Canada.
authors.forename: Ghassan
authors.initials: G
authors.lastname: Hamarneh
mesh_terms: D000465:Algorithms; D002584:Cervix Uteri / Q000166:cytology* / Q000000981:diagnostic imaging; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D065006:Papanicolaou Test / Q000379:methods; D002583:Uterine Cervical Neoplasms