Abstract
Automated segmentation of cells in cervical cytology images poses a great challenge due to the presence of fuzzy and overlapping cells, noisy background, and poor cytoplasmic contrast. We present an improved method for segmenting nuclei and cytoplasm from a cluster of cervical cells using convolutional network and fast multi-cell labeling. A light convolutional neural network (CNN) model is employed to generate nuclei candidates, which can serve as accurate initializations for the subsequent level set segmentation and provide a priori knowledge for the cytoplasm segmentation. A fast multi-cell labeling method based on the superpixel map is devised to roughly segment clumped and inhomogeneous cytoplasm before applying a cell boundary refinement approach. A shape constraint in conjunction with boundary and region information drive a level set formulation to perform a robust cell segmentation. The qualitative and quantitative evaluations demonstrated that the presented cellular segmentation method is effective and efficient.
T. Wan—This work was partially supported by the National Natural Science Foundation of China under award No. 61876197.
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Xu, S., Sang, C., Jin, Y., Wan, T. (2018). Robust Segmentation of Overlapping Cells in Cervical Cytology Using Light Convolution Neural Network. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11307. Springer, Cham. https://doi.org/10.1007/978-3-030-04239-4_35
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