Document Type

Article

Publication Date

2016

DOI

10.1093/bioinformatics/btw165

Publication Title

Bioinformatics

Volume

32

Issue

15

Pages

2352-2358

Abstract

Motivation: Accurate segmentation of brain electron microscopy (EM) images is a critical step in dense circuit reconstruction. Although deep neural networks (DNNs) have been widely used in a number of applications in computer vision, most of these models that proved to be effective on image classification tasks cannot be applied directly to EM image segmentation, due to the different objectives of these tasks. As a result, it is desirable to develop an optimized architecture that uses the full power of DNNs and tailored specifically for EM image segmentation.

Results: In this work, we proposed a novel design of DNNs for this task. We trained a pixel classifier that operates on raw pixel intensities with no preprocessing to generate probability values for each pixel being a membrane or not. Although the use of neural networks in image segmentation is not completely new, we developed novel insights and model architectures that allow us to achieve superior performance on EM image segmentation tasks. Our submission based on these insights to the 2D EM Image Segmentation Challenge achieved the best performance consistently across all the three evaluation metrics. This challenge is still ongoing and the results in this paper are as of June 5, 2015.

Comments

Web of Science: "Free full-text from publisher."

Original Publication Citation

Fakhry, A., Peng, H. C., & Ji, S. W. (2016). Deep models for brain em image segmentation: Novel insights and improved performance. Bioinformatics, 32(15), 2352-2358. doi:10.1093/bioinformatics/btw165

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