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Nuclei counting in microscopy images with three dimensional generative adversarial networks
Author(s): Shuo Han; Soonam Lee; Chichen Fu; Paul Salama; Kenneth W. Dunn; Edward J. Delp
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Paper Abstract

Microscopy image analysis can provide substantial information for clinical study and understanding of biological structures. Two-photon microscopy is a type of fluorescence microscopy that can image deep into tissue with near-infrared excitation light. We are interested in methods that can detect and characterize nuclei in 3D fluorescence microscopy image volumes. In general, several challenges exist for counting nuclei in 3D image volumes. These include “crowding” and touching of nuclei, overlapping of nuclei, and shape and size variances of the nuclei. In this paper, a 3D nuclei counter using two different generative adversarial networks (GAN) is proposed and evaluated. Synthetic data that resembles real microscopy image is generated with a GAN and used to train another 3D GAN that counts the number of nuclei. Our approach is evaluated with respect to the number of groundtruth nuclei and compared with common ways of counting used in the biological research. Fluorescence microscopy 3D image volumes of rat kidneys are used to test our 3D nuclei counter. The accuracy results of proposed nuclei counter are compared with the ImageJ’s 3D object counter (JACoP) and the 3D watershed. Both the counting accuracy and the object-based evaluation show that the proposed technique is successful for counting nuclei in 3D.

Paper Details

Date Published: 15 March 2019
PDF: 11 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109492Y (15 March 2019); doi: 10.1117/12.2512591
Show Author Affiliations
Shuo Han, Purdue Univ. (United States)
Soonam Lee, Purdue Univ. (United States)
Chichen Fu, Purdue Univ. (United States)
Paul Salama, Indiana Univ.-Purdue Univ. Indianapolis (United States)
Kenneth W. Dunn, Indiana Univ. School of Medicine (United States)
Edward J. Delp, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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