Deep learning for isotropic super-resolution from non-isotropic 3D electron microscopy.

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International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2017. MICCAI 2017. Lecture Notes in Computer Science. 2017 Sept 4;10434:arXiv:1706.03142. doi: 10.1007/978-3-319-66185-8_16

Deep learning for isotropic super-resolution from non-isotropic 3D electron microscopy. Saalfeld Lab

Heinrich L, Bogovic JA, Saalfeld S

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Abstract

The most sophisticated existing methods to generate 3D isotropic super-resolution (SR) from non-isotropic electron microscopy (EM) are based on learned dictionaries. Unfortunately, none of the existing methods generate practically satisfying results. For 2D natural images, recently developed super-resolution methods that use deep learning have been shown to significantly outperform the previous state of the art. We have adapted one of the most successful architectures (FSRCNN) for 3D super-resolution, and compared its performance to a 3D U-Net architecture that has not been used previously to generate super-resolution. We trained both architectures on artificially downscaled isotropic ground truth from focused ion beam milling scanning EM (FIB-SEM) and tested the performance for various hyperparameter settings.

Our results indicate that both architectures can successfully generate 3D isotropic super-resolution from non-isotropic EM, with the U-Net performing consistently better. We propose several promising directions for practical application.

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Preprint published in Arxiv on June 9, 2017: https://arxiv.org/abs/1706.03142.

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Deep learning for isotropic super-resolution from non-isotropic 3D electron microscopy.

Abstract

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