FIB/SEM tomography segmentation by optical flow estimation.

Area of Science:

• Materials Science
• Imaging and Microscopy
• Chemical Engineering

Background:

• Focused ion beam/scanning electron microscopy tomography (FIB/SEM tomography) is crucial for analyzing mesoporous materials in energy applications.
• Segmentation of pore space and solid material in FIB/SEM tomography is challenging due to shine-through artifacts.
• Existing segmentation methods, like gray-value thresholding, struggle with these artifacts.

Purpose of the Study:

• To introduce a novel optical flow-based method for segmenting mesoporous materials in FIB/SEM tomography.
• To leverage shine-through artifacts to improve segmentation accuracy.
• To evaluate the performance of the new method against existing techniques.

Main Methods:

• Development of an optical flow-based algorithm designed to interpret shine-through artifacts.
• Application of the method to tomographic datasets from a polymer electrolyte fuel cell catalyst layer and a lithium-ion battery composite electrode.
• Comparison of results with previously performed manual segmentations and gray-value thresholding.

Main Results:

• The optical flow-based method achieved 86.6% accuracy and 84.0% precision for segmenting a polymer electrolyte fuel cell catalyst layer.
• The method demonstrated significantly better segmentation results compared to gray-value threshold binarization for both tested datasets.
• Effective utilization of shine-through artifacts for improved pore space and solid material differentiation.

Conclusions:

• The optical flow-based method provides a robust and accurate solution for segmenting mesoporous materials in FIB/SEM tomography.
• This approach overcomes limitations of traditional methods, particularly in the presence of shine-through artifacts.
• The findings have implications for the characterization of materials in energy storage and conversion devices.