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Automated cell nucleus detection for large-volume electron microscopy of neural tissue | IEEE Conference Publication | IEEE Xplore
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Automated cell nucleus detection for large-volume electron microscopy of neural tissue


Abstract:

Volumetric electron microscopy techniques, such as serial block-face electron microscopy (SBEM), generate massive amounts of image data that are used for reconstructing n...Show More

Abstract:

Volumetric electron microscopy techniques, such as serial block-face electron microscopy (SBEM), generate massive amounts of image data that are used for reconstructing neural circuits. Typically, this requires time-intensive manual annotation of cells and their connections. To facilitate this analysis, we study the problem of automated detection of cell nuclei in a new SBEM dataset that contains cerebral cortex, white matter, and striatum from an adult mouse brain. The dataset was manually annotated to identify the locations of all 3309 cell nuclei in the volume. We make both dataset and annotations available here. Using a hybrid approach that combines interactive learning, morphological processing, and object level feature classification, we demonstrate automated detection of cell nuclei at 92.4% recall and 95.1% precision. These algorithms are not RAM-limited and can scale to arbitrarily large datasets.
Date of Conference: 29 April 2014 - 02 May 2014
Date Added to IEEE Xplore: 31 July 2014
Electronic ISBN:978-1-4673-1961-4

ISSN Information:

Conference Location: Beijing, China

References

References is not available for this document.