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

ABSTRACT:

Motivation

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.

Results

Here, we propose DeepEM3D, a deep learning method for segmenting 3D anisotropic brain electron microscopy images. In this method, the deep learning model can efficiently build feature representation and incorporate sufficient multi-scale contextual information. We propose employing a combination of n

SUBMITTER: Zeng T 

PROVIDER: S-EPMC6248556 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature