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 long-standing 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.

本文研究了在原位应力变形实验中利用定量透射电子显微镜（TEM）揭示金属晶格中线性缺陷 —— 位错的运动。通过定量比较现有的实例分割方法，并将位错分割结果转换为数学线条以进行定量分析，我们提出了一种基于长度的新颖质量度量指标，用于估计网络性能。我们的分割方法准确性高且适用于领域特定的后处理，此外，我们提出的物理度量方式在性能上表现更加稳定可靠。

TEM 图像中位错的实例分割

Instance Segmentation of Dislocations in TEM Images

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 TEM experiments can provide important insights
into how dislocations behave and move. During such experiments, the dislocation
microstructure is captured in form of videos. The analysis of individual video
frames 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-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 training images sometimes as too artificial,
our findings show that they can result in superior performance, particularly
regarding the generalizing of the Deep Learning models with respect to
different microstructures and imaging conditions. Additionally, we propose an
enhanced deep learning method optimized for segmenting overlapping or
intersecting dislocation lines. Upon testing this framework on four distinct
real datasets, we find that our synthetic training data are able to yield
high-quality results also on real images-even more so if fine-tune on a few
real images was done.

本研究提出了用于分割位错的合成训练数据的参数模型，并开发了一种优化分割重叠或相交位错线的深度学习方法，该方法在多种微结构和成像条件下都表现出高效性和优越性。