Automatic microscopic cell counting by use of deeply-supervised density regression model

Shenghua He, Kyaw T. Minn, Lilianna Solnica-krezel, Mark Anastasio, Hua Li

Research output: Contribution to conferencePaperpeer-review


Accurately counting cells in microscopic images is important for medical diagnoses and biological studies, but manual cell counting is very tedious, time-consuming, and prone to subjective errors, and automatic counting can be less accurate than desired. To improve the accuracy of automatic cell counting, we propose here a novel method that employs deeply-supervised density regression. A fully convolutional neural network (FCNN) serves as the primary FCNN for density map regression. Innovatively, a set of auxiliary FCNNs are employed to provide additional supervision for learning the intermediate layers of the primary CNN to improve network performance. In addition, the primary CNN is designed as a concatenating framework to integrate multi-scale features through shortcut connections in the network, which improves the granularity of the features extracted from the intermediate CNN layers and further supports the final density map estimation. The experimental results on immunofluorescent images of human embryonic stem cells demonstrate the superior performance of the proposed method over other state-of-the-art methods.
Original languageEnglish (US)
StatePublished - Mar 18 2019
EventDigital Pathology - San Diego, United States
Duration: Feb 16 2019Feb 21 2019


ConferenceDigital Pathology


  • Automatic cell counting
  • Concatenating network
  • Deeply-supervised learning
  • Density regression
  • Microscopic images

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


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