Abstract
Cryo-electron Tomography (cryo-ET) generates 3D visualization of cellular organization that allows biologists to analyze cellular structures in a near-native state with nano resolution. Recently, deep learning methods have demonstrated promising performance in classification and segmentation of macromolecule structures captured by cryo-ET, but training individual deep learning models requires large amounts of manually labeled and segmented data from previously observed classes. To perform classification and segmentation in the wild (i.e., with limited training data and with unseen classes), novel deep learning model needs to be developed to classify and segment unseen macromolecules captured by cryo-ET. In this paper, we develop a one-shot learning framework, called cryo-ET one-shot network (COS-Net), for simultaneous classification of macromolecular structure and generation of the voxel-level 3D segmentation, using only one training sample per class. Our experimental results on 22 macromolecule classes demonstrated that our COS-Net could efficiently classify macromolecular structures with small amounts of samples and produce accurate 3D segmentation at the same time.
Highlights
Cryo-Electron Tomography has made possible the observation of cellular organelles and macromolecular structures at nano-meter resolution with native conformations (Lucicet al., 2013)
Inspired by one-shot learning models which aim to learn information about object categories from one, or only a few training images (Fe-Fei et al, 2003; Koch et al, 2015), In this work, we develop a Cryo-ET One-Shot Network (COS-Net) that is able to (1) classify macromolecular structure using only a very small amount of samples, (2) simultaneously segment structural regions in a subtomogram based on the classification network, and (3) be readily and directly applied to classify and segment novel structures without needing to be re-trained
We developed a customized subtomogram processing pipeline to refine the coarse attention/segmentation from cryo-ET one-shot network (COS-Net) based on 3D Conditional Random Field (3D-CRF) (Krähenbühl and Koltun, 2011)
Summary
Cryo-Electron Tomography (cryo-ET) has made possible the observation of cellular organelles and macromolecular structures at nano-meter resolution with native conformations (Lucicet al., 2013). Cryo-ET can visualize both known and unknown cellular structures in situ and reveals their spatial and organizational relationships (Oikonomou and Jensen, 2017). Using cryo-ET, it is possible to capture 3D structural information of diverse macromolecular structures inside a given scanned sample. To analyze the macromolecular structures in cryo-ET, two major subsequent steps need to occur. We need to extract the subtomograms and average those that belong to the same macromolecular class, in order to generate a high Signal-to-Noise Ratio (SNR) subtomogram for clear visualization (Zhang, 2019). It is desirable to obtain the macromolecule segmentation in subtomograms to analyze the macromolecular structure parameters such as size distribution and
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