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@INPROCEEDINGS{Ruzaeva:1015186,
      author       = {Ruzaeva, Karina and Govind, Kishan and Legros, Marc and
                      Sandfeld, Stefan},
      title        = {{I}nstance {S}egmentation of {D}islocations in {TEM}
                      {I}mages},
      publisher    = {IEEE},
      reportid     = {FZJ-2023-03582},
      pages        = {1-6},
      year         = {2023},
      abstract     = {Quantitative Transmission Electron Microscopy (TEM) during
                      in-situ straining experiment is able to reveal the motion of
                      dislocations - linear defects in the crystal lattice of
                      metals. In the domain of materials science, the knowledge
                      about the location and movement of dislocations is important
                      for creating novel materials with superior properties. A
                      longstanding problem, however, is to identify the position
                      and extract the shape of dislocations, which would
                      ultimately help to create a digital twin of such materials.
                      In this work, we quantitatively compare state-of-the-art
                      instance segmentation methods, including Mask R-CNN and
                      YOLOv8. The dislocation masks as the results of the instance
                      segmentation are converted to mathematical lines, enabling
                      quantitative analysis of dislocation length and geometry -
                      important information for the domain scientist, which we
                      then propose to include as a novel length-aware quality
                      metric for estimating the network performance. Our
                      segmentation pipeline shows a high accuracy suitable for all
                      domain-specific, further post-processing. Additionally, our
                      physics-based metric turns out to perform much more
                      consistently than typically used pixel-wise metrics.},
      month         = {Jul},
      date          = {2023-07-02},
      organization  = {2023 IEEE 23rd International
                       Conference on Nanotechnology (NANO),
                       Jeju City (Korea), 2 Jul 2023 - 5 Jul
                       2023},
      cin          = {IAS-9},
      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)8},
      UT           = {WOS:001061580700068},
      doi          = {10.1109/NANO58406.2023.10231169},
      url          = {https://juser.fz-juelich.de/record/1015186},
}