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.
Original language | English |
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Title of host publication | 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 69-72 |
Number of pages | 4 |
ISBN (Electronic) | 9781467319591 |
DOIs | |
Publication status | Published - 29 Jul 2014 |
Externally published | Yes |
Event | 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China Duration: 29 Apr 2014 → 2 May 2014 |
Publication series
Name | 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 |
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Conference
Conference | 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 |
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Country/Territory | China |
City | Beijing |
Period | 29/04/14 → 2/05/14 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.
Keywords
- Automated nucleus detection
- Block-face electron microscopy
- Block-wise connected components
- Connectomics
- Interactive segmentation
- Random forest
- Soma