Supervised machine learning for region assignment of zebrafish brain nuclei based on computational assessment of cell neighborhoods

Samarth Gupta, Yuan Xue, Yifu Ding, Daniel Vanselow, Maksim Yakovlev, Damian B. van Rossum, Sharon X. Huang, Keith Cheng

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Histological studies provide cellular insights into tissue architecture and have been central to phenotyping and biological discovery. Synchrotron X-ray micro-tomography of tissue, or “X-ray histotomography”, yields three-dimensional reconstruction of fixed and stained specimens without sectioning. These reconstructions permit the computational creation of histology-like sections in any user-defined plane and slice thickness. Furthermore, they provide an exciting new basis for volumetric, computational histological phenotyping at cellular resolution. In this paper, we demonstrate the computational characterization of the zebrafish central nervous system imaged by Synchrotron X-ray micro-CT through the classification of small cellular neighborhood volumes centered at each detected nucleus in a 3D tomographic reconstruction. First, we propose a deep learning-based nucleus detector to detect nuclear centroids. We then develop, train, and test a convolutional neural network architecture for automatic classification of brain nuclei using five different neighborhood sizes containing 8, 12, 16, 20 and 24 isotropic voxels (0.743 x 0.743 x 0.743 μm each), corresponding to boxes with 5.944, 8.916, 11.89, 14.86, and 17.83 μm sides, respectively. We show that even with small cell neighborhoods, our proposed model is able to characterize brain nuclei into the major tissue regions with F1 score of 81.18% and sensitivity of 81.70%. Using our detector and classifier, we obtained very good results for fully segmenting major zebrafish brain regions in the 3D scan through patch wise labeling of cell neighborhoods.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationBiomedical Applications in Molecular, Structural, and Functional Imaging
EditorsAndrzej Krol, Barjor S. Gimi
ISBN (Electronic)9781510634015
StatePublished - 2020
EventMedical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging - Houston, United States
Duration: Feb 18 2020Feb 20 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

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

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