Automated cell nucleus detection for large-volume electron microscopy of neural tissue
Yükleniyor...
Dosyalar
Tarih
2014-04-29
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Automated nucleus detection, Block-face electron microscopy, Interactive segmentation, Random forest, Block-wise connected, Components, Connectomics, Soma, Identification, Brain
Kaynak
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Tek, F. B., Kroeger, T., Mikula, S. & Hamprecht, F. A. (2014). Automated cell nucleus detection for large-volume electron microscopy of neural tissue. Paper presented at the 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 69-72. doi:10.1109/ISBI.2014.6867811
