Interpolation for resizing 3D volumetric data (Tips and Tricks 50)

Interpolation for resizing 3D volumetric data (Tips and Tricks 50)

The video explains the process of interpolation on an input 3D image (array) to create a new image with adjusted pixel size and slice thickness by using RegularGridInterpolator from SciPy.

https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.RegularGridInterpolator.html

The code from this video is available here: https://github.com/bnsreenu/python_for_microscopists/blob/master/Tips_and_Tricks_50_interpolate_images_in_a_stack.ipynb

RegularGridInterpolator takes a 3D grid of values with certain intervals, adjusts the grid dimensions and fills in the values using interpolation to create a new 3D image with the desired pixel size and slice thickness.

FIB-SEM and volumetric EM data are typically acquired with specific pixel sizes and slice thicknesses where slice thickness usually containing lower resolution (thicker slices) compared to x/y pixel dimensions. Therefore, it may be necessary to change these parameters to match specific requirements, such as downstream analysis methods requiring isometric voxels. The code allows for adjusting the pixel size and slice thickness, ensuring the resulting data is consistent with the desired parameters.

Dataset from: https://paperswithcode.com/dataset/3d-platelet-em

Видео Interpolation for resizing 3D volumetric data (Tips and Tricks 50) канала DigitalSreeni