分子イメージングとダイナミクスのジャーナル
オープンアクセス

概要

Speeding up the Analysis of Neuron Morphology using Parallel Processing

Wang DD, Bourke D, Domanski L and Vallotton P

Researchers using High Content Screening systems generate thousands of fluorescently labeled cell images from which they measure subtle but important phenotypic changes summarized by dozens of parameters. Large image datasets and fast turnaround requirement have made the efficient High Content Analysis a challenging task. This paper studies multi-core based high performance image analysis and its application to data and compute-intensive High Content Analyses. A vertical parallelization strategy is employed and an automated parallelization framework is implemented to automatically dispatch image processing tasks. The strategy is based on allocation of different images to separate processors so that each image is analyzed sequentially on a single processor and multiple images are processed by separate processors in parallel. Experiments demonstrate that this approach, of a generic character, considerably increases throughput.