Constrained Multi-shape Evolution for Overlapping Cytoplasm Segmentation
- URL: http://arxiv.org/abs/2004.03892v2
- Date: Wed, 15 Apr 2020 12:00:02 GMT
- Title: Constrained Multi-shape Evolution for Overlapping Cytoplasm Segmentation
- Authors: Youyi Song, Lei Zhu, Baiying Lei, Bin Sheng, Qi Dou, Jing Qin, Kup-Sze
Choi
- Abstract summary: We present a novel and effective shape prior-based approach, called constrained multi-shape evolution.
It segments all overlapping cytoplasms in the clump simultaneously by jointly evolving each cytoplasm's shape guided by the modeled shape priors.
In the shape evolution, we compensate intensity deficiency for the segmentation by introducing not only the modeled local shape priors but also global shape priors.
- Score: 49.992392231966015
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Segmenting overlapping cytoplasm of cells in cervical smear images is a
clinically essential task, for quantitatively measuring cell-level features in
order to diagnose cervical cancer. This task, however, remains rather
challenging, mainly due to the deficiency of intensity (or color) information
in the overlapping region. Although shape prior-based models that compensate
intensity deficiency by introducing prior shape information (shape priors)
about cytoplasm are firmly established, they often yield visually implausible
results, mainly because they model shape priors only by limited shape
hypotheses about cytoplasm, exploit cytoplasm-level shape priors alone, and
impose no shape constraint on the resulting shape of the cytoplasm. In this
paper, we present a novel and effective shape prior-based approach, called
constrained multi-shape evolution, that segments all overlapping cytoplasms in
the clump simultaneously by jointly evolving each cytoplasm's shape guided by
the modeled shape priors. We model local shape priors (cytoplasm--level) by an
infinitely large shape hypothesis set which contains all possible shapes of the
cytoplasm. In the shape evolution, we compensate intensity deficiency for the
segmentation by introducing not only the modeled local shape priors but also
global shape priors (clump--level) modeled by considering mutual shape
constraints of cytoplasms in the clump. We also constrain the resulting shape
in each evolution to be in the built shape hypothesis set, for further reducing
implausible segmentation results. We evaluated the proposed method in two
typical cervical smear datasets, and the extensive experimental results show
that the proposed method is effective to segment overlapping cytoplasm,
consistently outperforming the state-of-the-art methods.
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