TY  - CONF
AU  - Ruzaeva, Karina
AU  - Govind, Kishan
AU  - Legros, Marc
AU  - Sandfeld, Stefan
TI  - Instance Segmentation of Dislocations in TEM Images
PB  - IEEE
M1  - FZJ-2023-03582
SP  - 1-6
PY  - 2023
AB  - 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.
T2  - 2023 IEEE 23rd International Conference on Nanotechnology (NANO)
CY  - 2 Jul 2023 - 5 Jul 2023, Jeju City (Korea)
Y2  - 2 Jul 2023 - 5 Jul 2023
M2  - Jeju City, Korea
LB  - PUB:(DE-HGF)8
UR  - <Go to ISI:>//WOS:001061580700068
DO  - DOI:10.1109/NANO58406.2023.10231169
UR  - https://juser.fz-juelich.de/record/1015186
ER  -