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Accurate and High Throughput Cell Segmentation Method for Mouse Brain Nuclei Using Cascaded Convolutional Neural Network

  • Qian WangEmail author
  • Shaoyu Wang
  • Xiaofeng Zhu
  • Tianyi Liu
  • Zachary Humphrey
  • Vladimir Ghukasyan
  • Mike Conway
  • Erik Scott
  • Giulia Fragola
  • Kira Bradford
  • Mark J. Zylka
  • Ashok Krishnamurthy
  • Jason L. Stein
  • Guorong Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10530)

Abstract

Recent innovations in tissue clearing and light sheet microscopy allow rapid acquisition of three-dimensional micron resolution images in fluorescently labeled brain samples. These data allow the observation of every cell in the brain, necessitating an accurate and high-throughput cell segmentation method in order to perform basic operations like counting number of cells within a region; however, large computational challenges given noise in the data and sheer number of features to identify. Inspired by the success of deep learning technique in medical imaging, we propose a supervised learning approach using convolution neural network (CNN) to learn the non-linear relationship between local image appearance (within an image patch) and manual segmentations (cell or background at the center of the underlying patch). In order to improve the segmentation accuracy, we further integrate high-level contextual features with low-level image appearance features. Specifically, we extract contextual features from the probability map of cells (output of current CNN) and train the next CNN based on both patch-wise image appearance and contextual features, extending previous methods into a cascaded approach. Using (a) high-level contextual features extracted from the cell probability map and (b) the spatial information of cell-to-cell locations, our cascaded CNN progressively improves the segmentation accuracy. We have evaluated the segmentation results on mouse brain images, and compared conventional image processing approaches. More accurate and robust segmentation results have been achieved with our cascaded CNN method, indicating the promising potential of our proposed cell segmentation method for use in large tissue cleared images.

Keywords

Convolutional neural network Contextual feature Cascade learning Cell segmentation Mouse microscopy image 

Notes

Acknowledgements

The research is supported by the National Science Foundation (NSF 1649916). The first author is supported by the China Scholarship Council for one year’s visiting at the University of North Carolina at Chapel Hill.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Qian Wang
    • 1
    • 2
    Email author
  • Shaoyu Wang
    • 2
  • Xiaofeng Zhu
    • 3
  • Tianyi Liu
    • 4
  • Zachary Humphrey
    • 4
  • Vladimir Ghukasyan
    • 4
  • Mike Conway
    • 5
  • Erik Scott
    • 5
  • Giulia Fragola
    • 4
  • Kira Bradford
    • 5
  • Mark J. Zylka
    • 4
  • Ashok Krishnamurthy
    • 5
  • Jason L. Stein
    • 4
    • 6
  • Guorong Wu
    • 2
  1. 1.School of Communication and Information EngineeringXi’an University of Posts and TelecommunicationsXi’anChina
  2. 2.BRIC and Department of RadiologyUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaUSA
  4. 4.Neuroscience CenterUniversity of North CarolinaChapel HillUSA
  5. 5.Renaissance Computing Institute (RENCI)Chapel HillUSA
  6. 6.Department of GeneticsUniversity of North CarolinaChapel HillUSA

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