An Automated Workflow for Segmenting Single Adult Cardiac Cells from Large-Volume Serial Block-Face Scanning Electron Microscopy Data
| Autor: | Eric Hanssen, Akter Hussain, Siavash Beikoghli Kalkhoran, Shouryadipta Ghosh, Derek J. Hausenloy, Vijay Rajagopal |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Adult Serial block-face scanning electron microscopy Computer science Scanning electron microscope Image processing Mitochondrion 03 medical and health sciences 0302 clinical medicine Single-cell analysis Structural Biology Image Processing Computer-Assisted Humans Myocytes Cardiac Polygon mesh Cluster analysis 030304 developmental biology 0303 health sciences Pixel business.industry Pattern recognition Image segmentation 030104 developmental biology Microscopy Electron Scanning Artificial intelligence Single-Cell Analysis Myofibril business 030217 neurology & neurosurgery Volume (compression) |
| DOI: | 10.1101/242701 |
| Popis: | 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 respectively, while the nuclei occupy 0.7% of the cell for which the entire volume was captured in the SBF-SEM dataset. Mitochondria clustering increased at the periphery of the nucleus region and branching points of the cardiac cell. The segmentations also showed high area fraction of mitochondria (up to 70% of the 2D image slice) in the sub-sarcolemmal region, whilst it was closer to 50% in the intermyofibrillar space. We finally demonstrate that our segmentations can be turned into 3D finite element meshes for cardiac cell computational physiology studies. We offer our large dataset and MATLAB implementation of the algorithm for research use atwww.github.com/CellSMB/sbfsem-cardiac-cell-segmenter/. We anticipate that this timely tool will be of use to cardiac computational and experimental physiologists alike who study cardiac ultrastructure and its role in heart function. |
| Databáze: | OpenAIRE |
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