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.