Modern fluorescence microscopy modalities, such as light-sheet microscopy, are capable of acquiring large three-dimensional images at high data rate. This creates a bottleneck in computational processing and analysis of the acquired images, as the rate of acquisition outpaces the speed of processing. Moreover, images can be so large that they do not fit the main memory of a single computer. We address both issues by developing a distributed parallel algorithm for segmentation of large fluorescence microscopy images. The method is based on the versatile Discrete Region Competition algorithm, which has previously proven useful in microscopy image segmentation. The present distributed implementation decomposes the input image into smaller sub-images that are distributed across multiple computers. Using network communication, the computers orchestrate the collectively solving of the global segmentation problem. This not only enables segmentation of large images (we test images of up to 1010 pixels), but also accelerates segmentation to match the time scale of image acquisition. Such acquisition-rate image segmentation is a prerequisite for the smart microscopes of the future and enables online data compression and interactive experiments.
|Original language||English (US)|
|State||Published - Apr 2016|
Bibliographical noteFunding Information:
This work was supported by the Max Planck Society and by the German Federal Ministry of Education and Research (BMBF) under funding code 031A099. We thank the Center for Information Services and High Performance Computing (ZIH) at TU Dresden for generous allocation of computer time. We thank Dr. Pavel Tomancak (MPI-CBG) for providing the example image of Drosophila melanogaster, and Dr. Jan Huisken (MPI-CBG) and Stephan Daetwyler (Huisken lab, MPI-CBG) for providing the example image of zebrafish vasculature. We also thank Pietro Incardona and Bevan Cheeseman (both MOSAIC group) for many discussions.
© 2016 Afshar, Sbalzarini. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.