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Open AccessArticle

An Adversarial and Densely Dilated Network for Connectomes Segmentation

by 1, 1, 1,2,* and 1,*
1
School of Software Enginnering, Tongji University, Shanghai 201804, China
2
Institute of Translational Medicine, Tongji University, Shanghai 201804, China
*
Authors to whom correspondence should be addressed.
Symmetry 2018, 10(10), 467; https://doi.org/10.3390/sym10100467
Received: 28 August 2018 / Revised: 5 October 2018 / Accepted: 8 October 2018 / Published: 9 October 2018
(This article belongs to the Special Issue Deep Learning-Based Biometric Technologies)
Automatic reconstructing of neural circuits in the brain is one of the most crucial studies in neuroscience. Connectomes segmentation plays an important role in reconstruction from electron microscopy (EM) images; however, it is rather challenging due to highly anisotropic shapes with inferior quality and various thickness. In our paper, we propose a novel connectomes segmentation framework called adversarial and densely dilated network (ADDN) to address these issues. ADDN is based on the conditional Generative Adversarial Network (cGAN) structure which is the latest advance in machine learning with power to generate images similar to the ground truth especially when the training data is limited. Specifically, we design densely dilated network (DDN) as the segmentor to allow a deeper architecture and larger receptive fields for more accurate segmentation. Discriminator is trained to distinguish generated segmentation from manual segmentation. During training, such adversarial loss function is optimized together with dice loss. Extensive experimental results demonstrate that our ADDN is effective for such connectomes segmentation task, helping to retrieve more accurate segmentation and attenuate the blurry effects of generated boundary map. Our method obtains state-of-the-art performance while requiring less computation on ISBI 2012 EM dataset and mouse piriform cortex dataset. View Full-Text
Keywords: electron microscopy images; connectomes segmentation; adversarial training; densely dilated network; cGAN electron microscopy images; connectomes segmentation; adversarial training; densely dilated network; cGAN
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Chen, K.; Zhu, D.; Lu, J.; Luo, Y. An Adversarial and Densely Dilated Network for Connectomes Segmentation. Symmetry 2018, 10, 467.

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