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

作者：Youyi Song, Ling Zhang, Siping Chen, Dong Sheng Ni, Baiying Lei, Tianfu Wang · 发表于：IEEE Transactions on Biomedical Engineering · 年份：2015 · DOI：10.1109/tbme.2015.2430895 · 被引用次数：278 · 研究领域：AI in cancer detection、Gene expression and cancer classification、Digital Imaging for Blood Diseases

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.