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 novel boundary map generation methods with optimized model ensembles to address the inherent challenges of segmenting anisotropic images. We evaluated our method by participating in the 3D segmentation of neurites in EM images (SNEMI3D) challenge. Our submission is ranked #1 on the current leaderboard as of Oct 15, 2016. More importantly, our result was very close to human-level performance in terms of the challenge evaluation metric: namely, a Rand error of 0.06015 versus the human value of 0.05998.
Availability and implementation: The code is available at https://github.com/divelab/deepem3d/.
Contact: sji@eecs.wsu.edu.
Supplementary information: Supplementary data are available at Bioinformatics online.
© The Author(s) 2017. Published by Oxford University Press.