Journal article

An automated workflow for segmenting single adult cardiac cells from large-volume serial block-face scanning electron microscopy data

Akter Hussain, Shouryadipta Ghosh, Siavash Beikoghli Kalkhoran, Derek J Hausenloy, Eric Hanssen, Vijay Rajagopal



This paper presents a new algorithm to automatically segment the myofibrils, mitochondria and nuclei within single adult cardiac cells that are part of a large serial-block-face scanning electron microscopy (SBF-SEM) dataset. The algorithm only requires a set of manually drawn contours that roughly demarcate the cell boundary at routine slice intervals (every 50th, for example). The algorithm correctly classified pixels within the single cell with 97% accuracy when compared to manual segmentations. One entire cell and the partial volumes of two cells were segmented. Analysis of segmentations within these cells showed that myofibrils and mitochondria occupied 47.5% and 51.6% on average respec..

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Awarded by Royal Society of New Zealand

Awarded by Australian Research Council

Awarded by British Heart Foundation

Funding Acknowledgements

The authors would like to acknowledge the support of the Royal Society of New Zealand Marsden Fast Start grant 11-UOA-186, Collier Charitable Fund and the Australian Research Council Discovery Project grant DP170101358. The authors acknowledge the support from the Trace Analysis for Chemical, Earth and Environmental Sciences (TrACEES) platform from the Melbourne Collaborative Infrastructure Research Program at the University of Melbourne and thank Dr. Jay Black (School of Earth Sciences) for operating the micro-CT scanner and processing data. We would like to thank Dr. Peter Munro from the UCL Institute of Ophthalmology for his help with tissue processing and imaging at University College London. We also thank Dr. Brian Glancy at NIH/NHLBI for valuable discussions and access to the data in Fig. 7. The FEI Teneo Volumescope was purchase thru the Australian Research Council Linkage Infrastructure, Equipment and Facility Scheme LE150100004.