AUTOMATED CELL SEGMENTATION WITH 3D FLUORESCENCE MICROSCOPY IMAGES

• Type of Material: Article
• Authors: Jun Kong, Emory University; Fusheng Wang, Emory University; George Teodoro, University of Brasília; Yanhui Liang, Emory University; Yangyang Zhu, Emory University; Carol Tucker-Burden, Emory University; Daniel Brat, Emory University
• Language: English
• Date: 2015-01-01
• Publisher: IEEE
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2015 IEEE.
• Final Published Version (URL): http://dx.doi.org/10.1109/ISBI.2015.7164091
• Title of Journal or Parent Work: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)
• Volume: 2015-July
• Start Page: 1212
• End Page: 1215
• Grant/Funding Information: This research is supported in part by grants from National Institute of Health K25CA181503 and R01CA176659, National Science Foundation ACI 1443054 and IIS 1350885, Georgia Research Alliance, and CNPq.
• Keywords: Engineering, Biomedical; 3D Cell Analysis; Radiology, Nuclear Medicine & Medical Imaging; Technology; Science & Technology; Fluorescence Microscopy Image; Gradient Vector Flow; Life Sciences & Biomedicine; Engineering, Electrical & Electronic; Engineering
• Research Categories: Engineering, Biomedical

Abstract

A large number of cell-oriented cancer investigations require an effective and reliable cell segmentation method on three dimensional (3D) fluorescence microscopic images for quantitative analysis of cell biological properties. In this paper, we present a fully automated cell segmentation method that can detect cells from 3D fluorescence microscopic images. Enlightened by fluorescence imaging techniques, we regulated the image gradient field by gradient vector flow (GVF) with interpolated and smoothed data volume, and grouped voxels based on gradient modes identified by tracking GVF field. Adaptive thresholding was then applied to voxels associated with the same gradient mode where voxel intensities were enhanced by a multiscale cell filter. We applied the method to a large volume of 3D fluorescence imaging data of human brain tumor cells with (1) small cell false detection and missing rates for individual cells; and (2) trivial over and under segmentation incidences for clustered cells. Additionally, the concordance of cell morphometry structure between automated and manual segmentation was encouraging. These results suggest a promising 3D cell segmentation method applicable to cancer studies.