Figure 3.
Schematics of some of the implemented components to achieve FIB-SEM automation and its results. (a) Automated Coincidence Point routine is illustrated schematically. When not tuned, the two beams are usually pointing at different positions of the sample surface (green plane, blue point for FIB center, red point for SEM center). The orange plane below shows the case where the ideal position (yellow point) is achieved for both FIB and SEM beams. In the software routine, a square is sputtered with the ion beam on the sample surface. The offset between the two beams is calculated based on the difference between the center of the sputtered mark in the SEM and FIB images (dy, distance between red and blue positions in the green plane). The z height (dz) of the stage is then corrected, and a further refinement using the SEM beam shift is performed by calculating the translation of the square mark between FIB (50 pA image) and SEM images. (b) Milling & Trench Detection: (1) After finding the coincidence point, a trench is milled to expose a cross-section at the region of interest. (2) The trench is detected to accurately position the field of view. First, three-level thresholding is applied to the image, followed by the detection of the biggest connected component that fits a trapezoid shape. From the final binary shape, boundaries of the trapezoid are found (3): the top corners (red circles), the trapezoid top center (blue circle), and the trapezoid center (light blue circle). (c) Image features detection: The image of the cross-section surface is analyzed and scored for the best focus positions to perform autofocus and autostigmatism. Features inside the image are found by using Harris corner detection and the variance of a small region surrounding each detected corner position. The initial features (red points) highlight the high contrast and complex areas of the imaging surface which usually cluster on cellular structures. Features are clustered and their centroids (green dots) are then filtered and prioritized to detect the first 6 ones suitable for AFAS (blue points). Due to the brightness/contrast settings to make the cell visible well inside the cross-section, the top surface of the sample above the cellular edge, which is covered with a gold coat, is only faintly visible. This region is excluded from the analysis of the cross-section to prevent autofocus outside the proper field of view. (d) Acquired data: Images are acquired at 200 nm intervals (in z) throughout the Golgi apparatus region. The resulting stack is used for 3D render and quantifications. (e) Multi-site images: Result of an experiment, where multiple targets had been acquired automatically across the full surface of the sample. Scale bars: (a) all 50 µm; (b) all 25 µm; (c) 5 µm; (d) slices all 2 µm, model 5 µm; (e) 500, 50 µm.

Schematics of some of the implemented components to achieve FIB-SEM automation and its results. (a) Automated Coincidence Point routine is illustrated schematically. When not tuned, the two beams are usually pointing at different positions of the sample surface (green plane, blue point for FIB center, red point for SEM center). The orange plane below shows the case where the ideal position (yellow point) is achieved for both FIB and SEM beams. In the software routine, a square is sputtered with the ion beam on the sample surface. The offset between the two beams is calculated based on the difference between the center of the sputtered mark in the SEM and FIB images (dy, distance between red and blue positions in the green plane). The z height (dz) of the stage is then corrected, and a further refinement using the SEM beam shift is performed by calculating the translation of the square mark between FIB (50 pA image) and SEM images. (b) Milling & Trench Detection: (1) After finding the coincidence point, a trench is milled to expose a cross-section at the region of interest. (2) The trench is detected to accurately position the field of view. First, three-level thresholding is applied to the image, followed by the detection of the biggest connected component that fits a trapezoid shape. From the final binary shape, boundaries of the trapezoid are found (3): the top corners (red circles), the trapezoid top center (blue circle), and the trapezoid center (light blue circle). (c) Image features detection: The image of the cross-section surface is analyzed and scored for the best focus positions to perform autofocus and autostigmatism. Features inside the image are found by using Harris corner detection and the variance of a small region surrounding each detected corner position. The initial features (red points) highlight the high contrast and complex areas of the imaging surface which usually cluster on cellular structures. Features are clustered and their centroids (green dots) are then filtered and prioritized to detect the first 6 ones suitable for AFAS (blue points). Due to the brightness/contrast settings to make the cell visible well inside the cross-section, the top surface of the sample above the cellular edge, which is covered with a gold coat, is only faintly visible. This region is excluded from the analysis of the cross-section to prevent autofocus outside the proper field of view. (d) Acquired data: Images are acquired at 200 nm intervals (in z) throughout the Golgi apparatus region. The resulting stack is used for 3D render and quantifications. (e) Multi-site images: Result of an experiment, where multiple targets had been acquired automatically across the full surface of the sample. Scale bars: (a) all 50 µm; (b) all 25 µm; (c) 5 µm; (d) slices all 2 µm, model 5 µm; (e) 500, 50 µm.

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