Rapid 3-D delineation of cell nuclei for high-content screening platforms. (1st February 2016)

• Record Type: Journal Article
• Authors: Gertych, Arkadiusz; Ma, Zhaoxuan; Tajbakhsh, Jian; Velásquez-Vacca, Adriana; Knudsen, Beatrice S.
• Is Part Of: Computers in biology and medicine. Volume 69(2016)
• Page Start: 328
• Page End: 338
• Publication Date: 2016-02-01
• Subjects: 3-D segmentation of nuclei -- Image processing -- High-content screening -- Bio-image informatics; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2015.04.025
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: High-resolution three-dimensional (3-D) microscopy combined with multiplexing of fluorescent labels allows high-content analysis of large numbers of cell nuclei. The full automation of 3-D screening platforms necessitates image processing algorithms that can accurately and robustly delineate nuclei in images with little to no human intervention. Imaging-based high-content screening was originally developed as a powerful tool for drug discovery. However, cell confluency, complexity of nuclear staining as well as poor contrast between nuclei and background result in slow and unreliable 3-D image processing and therefore negatively affect the performance of studying a drug response. Here, we propose a new method, 3D-RSD, to delineate nuclei by means of 3-D radial symmetries and test it on high-resolution image data of human cancer cells treated by drugs. The nuclei detection performance was evaluated by means of manually generated ground truth from 2351 nuclei (27 confocal stacks). When compared to three other nuclei segmentation methods, 3D-RSD possessed a better true positive rate of 83.3% and F -score of 0.895±0.045 ( p -value=0.047). Altogether, 3D-RSD is a method with a very good overall segmentation performance. Furthermore, implementation of radial symmetries offers good processing speed, and makes 3D-RSD less sensitive to staining patterns. In particular, the 3D-RSD method performs well in cell lines, which are often used in imaging-based HCS platforms and are afflicted by nuclear crowding and overlaps that hinder feature extraction. Highlights: We developed a 3D-RSD algorithm to detect nuclei in 3-D image space. 3D-RSD can effectively deal with highly confluent specimens. 3D-RSD is fast and more accurate than three other reference methods, and can be used in high-content screening tasks to evaluate drug efficacy in human cancer cell lines.