Evaluation of three algorithms for the segmentation of overlapping cervical cells.

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dc.contributor.authorLu, Zhi
dc.contributor.authorCarneiro, Gustavo
dc.contributor.authorBradley, Andrew P.
dc.contributor.authorUshizima, Daniela Mayumi
dc.contributor.authorNosrati, Masoud S.
dc.contributor.authorBianchi, Andrea Gomes Campos
dc.contributor.authorCarneiro, Cláudia Martins
dc.contributor.authorHamarneh, Ghassan
dc.date.accessioned2017-08-30T17:07:43Z
dc.date.available2017-08-30T17:07:43Z
dc.date.issued2017
dc.description.abstractIn 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 (ISBI) 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; the presence of debris and the large degree of cellular overlap. The challenge initially utilised 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 synthesised 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 all 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.pt_BR
dc.identifier.citationLU, Z. et al. Evaluation of three algorithms for the segmentation of overlapping cervical cells. IEEE Journal of Biomedical and Health Informatics, v. 1, p. 2168-2194, 2017. Disponível em: <https://ieeexplore.ieee.org/document/7386573>. Acesso em: 29 ago. 2017.pt_BR
dc.identifier.doihttps://doi.org/10.1109/JBHI.2016.2519686
dc.identifier.issn2168-2194
dc.identifier.urihttp://www.repositorio.ufop.br/handle/123456789/8583
dc.identifier.uri2https://ieeexplore.ieee.org/document/7386573pt_BR
dc.language.isoen_USpt_BR
dc.rightsrestritopt_BR
dc.subjectChallengept_BR
dc.titleEvaluation of three algorithms for the segmentation of overlapping cervical cells.pt_BR
dc.typeArtigo publicado em periodicopt_BR
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