Deep learning based object tracking for 3D microstructure reconstruction.

Journal: Methods (San Diego, Calif.)
Published Date: Apr 9, 2022

Abstract

In medical and material science, 3D reconstruction is of great importance for quantitative analysis of microstructures. After the image segmentation process of serial slices, in order to reconstruct each local structure in volume data, it needs to use precise object tracking algorithm to recognize the same object region in adjacent slice. Suffering from weak representative hand-crafted features, traditional object tracking methods always draw out under-segmentation results. In this work, we have proposed an adjacent similarity based deep learning tracking method (ASDLTrack) to reconstruct 3D microstructure. By transferring object tracking problem to classification problem, it can utilize powerful representative ability of convolutional neural network in pattern recognition. Experiments in three datasets with three metrics demonstrate that our algorithm achieves the promising performance compared to traditional methods.

Authors

  • Boyuan Ma
    Shunde Graduate School, University of Science and Technology Beijing, China; Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, China; Institute of Artificial Intelligence, University of Science and Technology Beijing, China; Beijing Key Laboratory of Knowledge Engineering for Materials Science, University of Science and Technology Beijing, China.
  • Yuting Xu
    Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ, United States.
  • Jiahao Chen
    The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310006, China.
  • Pan Puquan
    International School of Advanced Materials, South China University of Technology, China.
  • Xiaojuan Ban
    Shunde Graduate School, University of Science and Technology Beijing, China; Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, China; Institute of Artificial Intelligence, University of Science and Technology Beijing, China; Beijing Key Laboratory of Knowledge Engineering for Materials Science, University of Science and Technology Beijing, China. Electronic address: banxj@ustb.edu.cn.
  • Hao Wang
    Department of Cardiology, Second Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Weihua Xue
    School of Materials Science and Engineering, China; School of Materials Science and Technology, Liaoning Technical University, China.

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