Evaluation of three algorithms for the segmentation of overlapping cervical cells.
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2017
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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 (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.
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Citation
LU, 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.