Classification in Cryo-Electron Tomograms
Creators: name: Gubins, Ilja
name: Schot, Gijs Van Der
name: Veltkamp, Remco C.
name: Förster, Friedrich
name: Du, Xuefeng
name: Zeng, Xiangrui
name: Zhu, Zhenxi
name: Chang, Lufan
name: Xu, Min
name: Moebel, Emmanuel
name: Martinez-Sanchez, Antonio
name: Kervrann, Charles
name: Lai, Tuan M.
name: Han, Xusi
name: Terashi, Genki
name: Kihara, Daisuke
name: Himes, Benjamin A.
name: Wan, Xiaohua
name: Zhang, Jingrong
name: Gao, Shan
name: Hao, Yu
name: Lv, Zhilong
name: Wan, Xiaohua
name: Yang, Zhidong
name: Ding, Zijun
name: Cui, Xuefeng
name: Zhang, Fa
Publisher: name: The Eurographics Association
Year: 2019
Subjects: Information systems
Evaluation of retrieval results
Specialized information retrieval
Multimedia and multimodal retrieval
Retrieval models and ranking

description: Different imaging techniques allow us to study the organization of life at different scales. Cryo-electron tomography (cryo-ET) has the ability to three-dimensionally visualize the cellular architecture as well as the structural details of macro-molecular assemblies under near-native conditions. Due to beam sensitivity of biological samples, an inidividual tomogram has a maximal resolution of 5 nanometers. By averaging volumes, each depicting copies of the same type of a molecule, resolutions beyond 4 Å have been achieved. Key in this process is the ability to localize and classify the components of interest, which is challenging due to the low signal-to-noise ratio. Innovation in computational methods remains key to mine biological information from the tomograms. To promote such innovation, we organize this SHREC track and provide a simulated dataset with the goal of establishing a benchmark in localization and classification of biological particles in cryo-electron tomograms. The publicly available dataset contains ten reconstructed tomograms obtained from a simulated cell-like volume. Each volume contains twelve different types of proteins, varying in size and structure. Participants had access to 9 out of 10 of the cell-like ground-truth volumes for learning-based methods, and had to predict protein class and location in the test tomogram. Five groups submitted eight sets of results, using seven different methods. While our sample size gives only an anecdotal overview of current approaches in cryo-ET classification, we believe it shows trends and highlights interesting future work areas. The results show that learning-based approaches is the current trend in cryo-ET classification research and specifically end-to-end 3D learning-based approaches achieve the best performance.
description: Eurographics Workshop on 3D Object Retrieval
description: SHREC Session 1
description: 49
description: 54
description: Ilja Gubins, Gijs van der Schot, Remco C. Veltkamp, Friedrich Förster, Xuefeng Du, Xiangrui Zeng, Zhenxi Zhu, Lufan Chang, Min Xu, Emmanuel Moebel, Antonio Martinez-Sanchez, Charles Kervrann, Tuan M. Lai, Xusi Han, Genki Terashi, Daisuke Kihara, Benjamin A. Himes, Xiaohua Wan, Jingrong Zhang, Shan Gao, Yu Hao, Zhilong Lv, Xiaohua Wan, Zhidong Yang, Zijun Ding, Xuefeng Cui, and Fa Zhang
description: CCS Concepts: Information systems --&gt; Evaluation of retrieval results; Specialized information retrieval; Multimedia and multimodal retrieval; Retrieval models and ranking