Supervised Machine Learning for Region Assignment of Zebrafish Brain Nuclei based on Computational Assessment of Cell Neighborhoods
Open Access
- Author:
- Gupta, Samarth
- Graduate Program:
- Information Sciences and Technology
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- March 24, 2020
- Committee Members:
- Sharon Xiaolei Huang, Thesis Advisor/Co-Advisor
Mary Beth Rosson, Program Head/Chair
Suhang Wang, Committee Member
Fenglong Ma, Committee Member - Keywords:
- Deep Learning
Microtomography - Abstract:
- 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 implement 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 into even different tissue regions 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 81.14% and sensitivity of 80.53%. 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.