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Robust Segmentation of Overlapping Cells in Cervical Cytology Using Light Convolution Neural Network

  • Shusong Xu
  • Chen Sang
  • Yulan Jin
  • Tao Wan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11307)

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.

Keywords

Nuclei detection Cytoplasm segmentation Cervical cytology Convolutional Neural Network Multi-cell labeling 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Biomedical Science and Medical Engineering, Beijing Advanced Innovation Centre for Biomedical EngineeringBeihang UniversityBeijingChina
  2. 2.Department of Pathology, Beijing Obstetrics and Gynecology HospitalCapital Medical UniversityBeijingChina

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