Segmentation of Overlapping Cytoplasm in Cervical Smear Images via Adaptive Shape Priors Extracted from Contour Fragments.

We present a novel approach to segmenting overlapping cytoplasm of cells in cervical smear images by leveraging adaptive shape priors extracted from cytoplasm's contour fragments and shape statistics. The main challenge of this task is that many occluded boundaries in cytoplasm clumps are extremely difficult to be identified, sometimes even visually indistinguishable. Given a clump where multiple cytoplasms overlap, our method starts by cutting its contour into a set of contour fragments. We then locate the corresponding contour fragments of each cytoplasm by a grouping process. For each cytoplasm, according to the grouped fragments and a set of known shape references, we construct its shape and then connect the fragments to form a closed contour as the segmentation result, which is explicitly constrained by the constructed shape. We further integrate intensity and curvature information, which is complementary to the shape priors extracted from contour fragments, into our framework to improve the segmentation accuracy. We propose to iteratively conduct fragments grouping, shape constructing, and fragments connecting, for progressively refining the shape priors and improving the segmentation results. We extensively evaluate the effectiveness of our method on two typical cervical smear datasets. Experimental results demonstrate that our approach is highly effective and consistently outperforms the state-of-theart approaches. The proposed method is general enough to be applied to other similar microscopic image segmentation tasks where heavily overlapped objects exist.