论文信息 - Automatic detection of synaptic partners in a whole-brain Drosophila electron microscopy data set - 字舞流文

Automatic detection of synaptic partners in a whole-brain Drosophila electron microscopy data set

Srinivas C. Turaga | Julia M. Buhmann | G. Jefferis | S. Saalfeld | D. Bock | W. Lee | S. Gerhard | Tom Kazimiers | P. Schlegel | Rachel I. Wilson | Jan Funke | Arlo Sheridan | Matthew Cook | Larissa Heinrich | Tri M. Nguyen | Caroline Malin-Mayor | R. Krause

[1] Feng Li,et al. A connectome and analysis of the adult Drosophila central brain , 2020, bioRxiv.

[2] Qian Kemao,et al. Y-Net: a one-to-two deep learning framework for digital holographic reconstruction. , 2019, Optics letters.

[3] Chandan Singh,et al. Large Scale Image Segmentation with Structured Loss Based Deep Learning for Connectome Reconstruction , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4] Thomas Brox,et al. U-Net: deep learning for cell counting, detection, and morphometry , 2018, Nature Methods.

[5] Eric T. Trautman,et al. A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster , 2017, Cell.

[6] G. Urban,et al. Automated synaptic connectivity inference for volume electron microscopy , 2017, Nature Methods.

[7] Casey M. Schneider-Mizell,et al. Quantitative neuroanatomy for connectomics in Drosophila , 2015, bioRxiv.

[8] Kevin L. Briggman,et al. Structural neurobiology: missing link to a mechanistic understanding of neural computation , 2012, Nature Reviews Neuroscience.