Learning Rotation Invariant Features for Cryogenic Electron Microscopy Image Reconstruction

• URL: http://arxiv.org/abs/2101.03549v1
• Date: Sun, 10 Jan 2021 13:38:16 GMT
• Title: Learning Rotation Invariant Features for Cryogenic Electron Microscopy Image Reconstruction
• Authors: Koby Bibas, Gili Weiss-Dicker, Dana Cohen, Noa Cahan, Hayit Greenspan
• Abstract summary: Cryo-Electron Microscopy (Cryo-EM) is a Nobel prize-winning technology for determining the 3D structure of particles at near-atomic resolution. Most approaches use discrete clustering which fails to capture the continuous nature of image rotation. We propose a novel method that leverages the recent development in the generative adversarial networks.
• Score: 1.9756768370987245
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cryo-Electron Microscopy (Cryo-EM) is a Nobel prize-winning technology for determining the 3D structure of particles at near-atomic resolution. A fundamental step in the recovering of the 3D single-particle structure is to align its 2D projections; thus, the construction of a canonical representation with a fixed rotation angle is required. Most approaches use discrete clustering which fails to capture the continuous nature of image rotation, others suffer from low-quality image reconstruction. We propose a novel method that leverages the recent development in the generative adversarial networks. We introduce an encoder-decoder with a rotation angle classifier. In addition, we utilize a discriminator on the decoder output to minimize the reconstruction error. We demonstrate our approach with the Cryo-EM 5HDB and the rotated MNIST datasets showing substantial improvement over recent methods.

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