Improved Nuclear Segmentation on Histopathology Images Using a Combination of Deep Learning and Active Contour Model

  • Lei Zhao
  • Tao WanEmail author
  • Hongxiang Feng
  • Zengchang QinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)


Automated nuclear segmentation on histopathological images is a prerequisite for a computer-aided diagnosis system. It becomes a challenging problem due to the nucleus occlusion, shape variation, and image background complexity. We present a computerized method for automatically segmenting nuclei in breast histopathology using an integration of a deep learning framework and an improved hybrid active contour (AC) model. A class of edge patches (nuclear boundary), in addition to the two usual classes - background patches and nuclei patches, are used to train a deep convolutional neural network (CNN) to provide accurate initial nuclear locations for the hybrid AC model. We devise a local-to-global scheme through incorporating the local image attributes in conjunction with region and boundary information to achieve robust nuclear segmentation. The experimental results demonstrated that the combination of CNN and AC model was able to gain improved performance in separating both isolated and overlapping nuclei.


Convolutional neural network Active contour model Nuclear segmentation Histopathology 


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© 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 General Thoracic SurgeryChina Japan Friendship HospitalBeijingChina
  3. 3.Intelligent Computing and Machine Learning Lab, School of ASEEBeihang UniversityBeijingChina

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