Accurate Segmentation of Cervical Cytoplasm and Nuclei Based on Multiscale Convolutional Network and Graph Partitioning

In this paper, a multiscale convolutional network (MSCN) and graph-partitioning-based method is proposed for accurate segmentation of cervical cytoplasm and nuclei. Specifically, deep learning via the MSCN is explored to extract scale invariant features, and then, segment regions centered at each pi...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2015-10, Vol.62 (10), p.2421-2433
Main Authors: Song, Youyi, Zhang, Ling, Chen, Siping, Ni, Dong, Lei, Baiying, Wang, Tianfu
Format: Article
Language:English
Subjects:
Cell Nucleus - chemistry
Cervical segmentation
Cervix Uteri - cytology
Cervix Uteri - pathology
coarse to fine
Computer architecture
Cytoplasm - chemistry
Feature extraction
Female
graph-partitioning
Histocytochemistry
Humans
Image color analysis
Image edge detection
Image Processing, Computer-Assisted - methods
Image segmentation
Microprocessors
Microscopy
multi-scale convolutional network
Shape
touching-cell splitting
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Summary:In this paper, a multiscale convolutional network (MSCN) and graph-partitioning-based method is proposed for accurate segmentation of cervical cytoplasm and nuclei. Specifically, deep learning via the MSCN is explored to extract scale invariant features, and then, segment regions centered at each pixel. The coarse segmentation is refined by an automated graph partitioning method based on the pretrained feature. The texture, shape, and contextual information of the target objects are learned to localize the appearance of distinctive boundary, which is also explored to generate markers to split the touching nuclei. For further refinement of the segmentation, a coarse-to-fine nucleus segmentation framework is developed. The computational complexity of the segmentation is reduced by using superpixel instead of raw pixels. Extensive experimental results demonstrate that the proposed cervical nucleus cell segmentation delivers promising results and outperforms existing methods.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2015.2430895