Automatic segmentation of high-throughput RNAi fluorescent cellular images

Pingkum Yan, Xiaobo Zhou, Mubarak Shah, Stephen T.C. Wong

Research output: Contribution to journalArticle

134 Scopus citations

Abstract

High-throughput genome-wide RNA interference (RNAi) screening is emerging as an essential tool to assist biologists in understanding complex cellular processes. The large number of images produced in each study make manual analysis intractable; hence, automatic cellular image analysis becomes an urgent need, where segmentation is the first and one of the most important steps. In this paper, a fully automatic method for segmentation of cells from genome-wide RNAi screening images is proposed. Nuclei are first extracted from the DNA channel by using a modified watershed algorithm. Cells are then extracted by modeling the interaction between them as well as combining both gradient and region information in the Actin and Rac channels. A new energy functional is formulated based on a novel interaction model for segmenting tightly clustered cells with significant intensity variance and specific phenotypes. The energy functional is minimized by using a multiphase level set method, which leads to a highly effective cell segmentation method. Promising experimental results demonstrate that automatic segmentation of high-throughput genome-wide multichannel screening can be achieved by using the proposed method, which may also be extended to other multichannel image segmentation problems.

Original languageEnglish (US)
Pages (from-to)109-117
Number of pages9
JournalIEEE Transactions on Information Technology in Biomedicine
Volume12
Issue number1
DOIs
StatePublished - Jan 2008

Keywords

  • Fluorescent microscopy
  • High throughput
  • Image segmentation
  • Interaction model
  • Level set
  • Multichannel

ASJC Scopus subject areas

  • Health Informatics
  • Health Information Management
  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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