Classification in Cryo-Electron Tomograms

dc.contributor.authorGubins, Iljaen_US
dc.contributor.authorSchot, Gijs van deren_US
dc.contributor.authorVeltkamp, Remco C.en_US
dc.contributor.authorFörster, Friedrichen_US
dc.contributor.authorDu, Xuefengen_US
dc.contributor.authorZeng, Xiangruien_US
dc.contributor.authorZhu, Zhenxien_US
dc.contributor.authorChang, Lufanen_US
dc.contributor.authorXu, Minen_US
dc.contributor.authorMoebel, Emmanuelen_US
dc.contributor.authorMartinez-Sanchez, Antonioen_US
dc.contributor.authorKervrann, Charlesen_US
dc.contributor.authorLai, Tuan M.en_US
dc.contributor.authorHan, Xusien_US
dc.contributor.authorTerashi, Genkien_US
dc.contributor.authorKihara, Daisukeen_US
dc.contributor.authorHimes, Benjamin A.en_US
dc.contributor.authorWan, Xiaohuaen_US
dc.contributor.authorZhang, Jingrongen_US
dc.contributor.authorGao, Shanen_US
dc.contributor.authorHao, Yuen_US
dc.contributor.authorLv, Zhilongen_US
dc.contributor.authorWan, Xiaohuaen_US
dc.contributor.authorYang, Zhidongen_US
dc.contributor.authorDing, Zijunen_US
dc.contributor.authorCui, Xuefengen_US
dc.contributor.authorZhang, Faen_US
dc.contributor.editorBiasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remcoen_US
dc.description.abstractDifferent 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.en_US
dc.description.sectionheadersSHREC Session 1
dc.description.seriesinformationEurographics Workshop on 3D Object Retrieval
dc.publisherThe Eurographics Associationen_US
dc.subjectInformation systems
dc.subjectEvaluation of retrieval results
dc.subjectSpecialized information retrieval
dc.subjectMultimedia and multimodal retrieval
dc.subjectRetrieval models and ranking
dc.titleClassification in Cryo-Electron Tomogramsen_US
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