Browsing by Author "Xu, Min"

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    Classification in Cryo-Electron Tomograms
    (The Eurographics Association, 2019) Gubins, Ilja; Schot, Gijs van der; Veltkamp, Remco C.; Förster, Friedrich; Du, Xuefeng; Zeng, Xiangrui; Zhu, Zhenxi; Chang, Lufan; Xu, Min; Moebel, Emmanuel; Martinez-Sanchez, Antonio; Kervrann, Charles; Lai, Tuan M.; Han, Xusi; Terashi, Genki; Kihara, Daisuke; Himes, Benjamin A.; Wan, Xiaohua; Zhang, Jingrong; Gao, Shan; Hao, Yu; Lv, Zhilong; Wan, Xiaohua; Yang, Zhidong; Ding, Zijun; Cui, Xuefeng; Zhang, Fa; Biasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remco
    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.
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    SeqDynamics: Visual Analytics for Evaluating Online Problem-solving Dynamics
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Xia, Meng; Xu, Min; Lin, Chuan-en; Cheng, Ta Ying; Qu, Huamin; Ma, Xiaojuan; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
    Problem-solving dynamics refers to the process of solving a series of problems over time, from which a student's cognitive skills and non-cognitive traits and behaviors can be inferred. For example, we can derive a student's learning curve (an indicator of cognitive skill) from the changes in the difficulty level of problems solved, or derive a student's self-regulation patterns (an example of non-cognitive traits and behaviors) based on the problem-solving frequency over time. Few studies provide an integrated overview of both aspects by unfolding the problem-solving process. In this paper, we present a visual analytics system named SeqDynamics that evaluates students' problem-solving dynamics from both cognitive and non-cognitive perspectives. The system visualizes the chronological sequence of learners' problem-solving behavior through a set of novel visual designs and coordinated contextual views, enabling users to compare and evaluate problem-solving dynamics on multiple scales. We present three scenarios to demonstrate the usefulness of SeqDynamics on a real-world dataset which consists of thousands of problem-solving traces. We also conduct five expert interviews to show that SeqDynamics enhances domain experts' understanding of learning behavior sequences and assists them in completing evaluation tasks efficiently.
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    SHREC 2021: Classification in Cryo-electron Tomograms
    (The Eurographics Association, 2021) Gubins, Ilja; Chaillet, Marten L.; Schot, Gijs van der; Trueba, M. Cristina; Veltkamp, Remco C.; Förster, Friedrich; Wang, Xiao; Kihara, Daisuke; Moebel, Emmanuel; Nguyen, Nguyen P.; White, Tommi; Bunyak, Filiz; Papoulias, Giorgos; Gerolymatos, Stavros; Zacharaki, Evangelia I.; Moustakas, Konstantinos; Zeng, Xiangrui; Liu, Sinuo; Xu, Min; Wang, Yaoyu; Chen, Cheng; Cui, Xuefeng; Zhang, Fa; Biasotti, Silvia and Dyke, Roberto M. and Lai, Yukun and Rosin, Paul L. and Veltkamp, Remco C.
    Cryo-electron tomography (cryo-ET) is an imaging technique that allows three-dimensional visualization of macro-molecular assemblies under near-native conditions. Cryo-ET comes with a number of challenges, mainly low signal-to-noise and inability to obtain images from all angles. Computational methods are key to analyze cryo-electron tomograms. To promote innovation in computational methods, we generate a novel simulated dataset to benchmark different methods of localization and classification of biological macromolecules in tomograms. Our publicly available dataset contains ten tomographic reconstructions of simulated cell-like volumes. Each volume contains twelve different types of complexes, varying in size, function and structure. In this paper, we have evaluated seven different methods of finding and classifying proteins. Seven research groups present results obtained with learning-based methods and trained on the simulated dataset, as well as a baseline template matching (TM), a traditional method widely used in cryo-ET research. We show that learning-based approaches can achieve notably better localization and classification performance than TM. We also experimentally confirm that there is a negative relationship between particle size and performance for all methods.