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@ARTICLE{Govind:1021215,
      author       = {Govind, Kishan and Oliveros, Daniela and Dlouhy, Antonin
                      and Legros, Marc and Sandfeld, Stefan},
      title        = {{D}eep learning of crystalline defects from {TEM} images: a
                      solution for the problem of ‘never enough training
                      data’},
      journal      = {Machine learning: science and technology},
      volume       = {5},
      number       = {1},
      issn         = {2632-2153},
      address      = {Bristol},
      publisher    = {IOP Publishing},
      reportid     = {FZJ-2024-00656},
      pages        = {015006 -},
      year         = {2024},
      abstract     = {Crystalline defects, such as line-like dislocations, play
                      an important role for the performance and reliability of
                      many metallic devices. Their interaction and evolution still
                      poses a multitude of open questions to materials science and
                      materials physics. In-situ transmission electron microscopy
                      (TEM) experiments can provide important insights into how
                      dislocations behave and move. The analysis of individual
                      video frames from such experiments can provide useful
                      insights but is limited by the capabilities of automated
                      identification, digitization, and quantitative extraction of
                      the dislocations as curved objects. The vast amount of data
                      also makes manual annotation very time consuming, thereby
                      limiting the use of deep learning (DL)-based, automated
                      image analysis and segmentation of the dislocation
                      microstructure. In this work, a parametric model for
                      generating synthetic training data for segmentation of
                      dislocations is developed. Even though domain scientists
                      might dismiss synthetic images as artificial, our findings
                      show that they can result in superior performance.
                      Additionally, we propose an enhanced DL method optimized for
                      segmenting overlapping or intersecting dislocation lines.
                      Upon testing this framework on four distinct real datasets,
                      we find that a model trained only on synthetic training data
                      can also yield high-quality results on real images–even
                      more so if the model is further fine-tuned on a few real
                      images. Our approach demonstrates the potential of synthetic
                      data in overcoming the limitations of manual annotation of
                      TEM image data of dislocation microstructure, paving the way
                      for more efficient and accurate analysis of dislocation
                      microstructures. Last but not least, segmenting such thin,
                      curvilinear structures is a task that is ubiquitous in many
                      fields, which makes our method a potential candidate for
                      other applications as well.},
      cin          = {IAS-9},
      ddc          = {621.3},
      cid          = {I:(DE-Juel1)IAS-9-20201008},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001142818000001},
      doi          = {10.1088/2632-2153/ad1a4e},
      url          = {https://juser.fz-juelich.de/record/1021215},
}