Automated detection of retinal cell nuclei in 3D micro-CT images of zebrafish using support vector machine classification

Yifu Ding, Thomas Tavolara, Keith Cheng

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

Abstract

Our group is developing a method to examine biological specimens in cellular detail using synchrotron microCT. The method can acquire 3D images of tissue at micrometer-scale resolutions, allowing for individual cell types to be visualized in the context of the entire specimen. For model organism research, this tool will enable the rapid characterization of tissue architecture and cellular morphology from every organ system. This characterization is critical for proposed and ongoing "phenome" projects that aim to phenotype whole-organism mutants and diseased tissues from different organisms including humans. With the envisioned collection of hundreds to thousands of images for a phenome project, it is important to develop quantitative image analysis tools for the automated scoring of organism phenotypes across organ systems. Here we present a first step towards that goal, demonstrating the use of support vector machines (SVM) in detecting retinal cell nuclei in 3D images of wild-Type zebrafish. In addition, we apply the SVM classifier on a mutant zebrafish to examine whether SVMs can be used to capture phenotypic differences in these images. The longterm goal of this work is to allow cellular and tissue morphology to be characterized quantitatively for many organ systems, at the level of the whole-organism.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2016
Subtitle of host publicationDigital Pathology
EditorsAnant Madabhushi, Metin N. Gurcan
PublisherSPIE
ISBN (Electronic)9781510600263
DOIs
StatePublished - Jan 1 2016
Event4th Medical Imaging 2016: Digital Pathology - San Diego, United States
Duration: Mar 2 2016Mar 3 2016

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume9791
ISSN (Print)1605-7422

Other

Other4th Medical Imaging 2016: Digital Pathology
CountryUnited States
CitySan Diego
Period3/2/163/3/16

All Science Journal Classification (ASJC) codes

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

Fingerprint Dive into the research topics of 'Automated detection of retinal cell nuclei in 3D micro-CT images of zebrafish using support vector machine classification'. Together they form a unique fingerprint.

  • Cite this

    Ding, Y., Tavolara, T., & Cheng, K. (2016). Automated detection of retinal cell nuclei in 3D micro-CT images of zebrafish using support vector machine classification. In A. Madabhushi, & M. N. Gurcan (Eds.), Medical Imaging 2016: Digital Pathology [97911A] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9791). SPIE. https://doi.org/10.1117/12.2216940