DeepEM3D: approaching human-level performance on 3D anisotropic EM image segmentation | Zendy

Tao Zeng | Zendy; Bian Wu | Zendy; Shuiwang Ji | Zendy

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DeepEM3D: approaching human-level performance on 3D anisotropic EM image segmentation

Author(s) -

Tao Zeng,

Bian Wu,

Shuiwang Ji

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx188

Subject(s) - lagging , computer science , throughput , segmentation , artificial intelligence , reliability (semiconductor) , computer vision , image (mathematics) , image segmentation , pattern recognition (psychology) , mathematics , telecommunications , power (physics) , statistics , physics , quantum mechanics , wireless

Progress in 3D electron microscopy (EM) imaging has greatly facilitated neuroscience research in high-throughput data acquisition. Correspondingly, high-throughput automated image analysis methods are necessary to work on par with the speed of data being produced. One such example is the need for automated EM image segmentation for neurite reconstruction. However, the efficiency and reliability of current methods are still lagging far behind human performance.

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