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

Authors:
Dr. Ling Zhang, PhD
Dr. Ling Zhang, PhD
University of Iowa
Iowa city, IA | United States

IEEE Trans Biomed Eng 2015 Oct 7;62(10):2421-33. Epub 2015 May 7.

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.

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Source
http://dx.doi.org/10.1109/TBME.2015.2430895DOI Listing
October 2015
71 Reads
7 Citations
2.350 Impact Factor

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