Morphologically constrained and data informed cell segmentation of budding yeast

被引:24
作者
Bakker, Elco [1 ,2 ]
Swain, Peter S. [1 ,2 ]
Crane, Matthew M. [1 ,2 ]
机构
[1] Univ Edinburgh, SynthSys Synthet & Syst Biol, Edinburgh EH9 3BF, Midlothian, Scotland
[2] Univ Edinburgh, Sch Biol Sci, Edinburgh EH9 3BF, Midlothian, Scotland
基金
英国生物技术与生命科学研究理事会;
关键词
SHAPE PRIORS; DYNAMICS; TRACKING; LIFE;
D O I
10.1093/bioinformatics/btx550
中图分类号
Q5 [生物化学];
学科分类号
070307 [化学生物学];
摘要
Motivation: Although high-content image cytometry is becoming increasingly routine, processing the large amount of data acquired during time-lapse experiments remains a challenge. The majority of approaches for automated single-cell segmentation focus on flat, uniform fields of view covered with a single layer of cells. In the increasingly popular microfluidic devices that trap individual cells for long term imaging, these conditions are not met. Consequently, most techniques for segmentation perform poorly. Although potentially constraining the generalizability of software, incorporating information about the microfluidic features, flow of media and the morphology of the cells can substantially improve performance. Results: Here we present DISCO (Data Informed Segmentation of Cell Objects), a framework for using the physical constraints imposed by microfluidic traps, the shape based morphological constraints of budding yeast and temporal information about cell growth and motion to allow tracking and segmentation of cells in microfluidic devices. Using manually curated datasets, we demonstrate substantial improvements in both tracking and segmentation when compared with existing software. Availability and implementation: The MATLAB code for the algorithm and for measuring performance is available at https://github.com/pswain/segmentation-software and the test images and the curated ground-truth results used for comparing the algorithms are available at http://datashare.is.ed.ac.uk/handle/10283/2002. Contact: mcrane2@uw.edu Supplementary information: Supplementary data are available at Bioinformatics online.
引用
收藏
页码:88 / 96
页数:9
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