Augmentation Parameters.csv
This tool use a csv file for storing the parameters of image augmentations.
A technique to expand the variety of dataset.
When number (and size) of images to train the network are few, the network could over-fit on your training images, and have bad performance on images it didn't see before.
By apply various techniques including crop, flip, rotation and so on, you can generate many variations of your training images, which helps to overcome the over-fit issue.
Rotate the image at xy/xz/yz plane respectively, with a random angle between Low Bound and High Bound.
Note if your images are anisotropic (depth resolution is different to xy resolution), you should not enable xz/yz plane rotation.
Self Explanatory.
Each cropped patch can be randomly scaled with a scale factor between Low Bound and High Bound.
Note this won't affect the size of each cropped patches.
Lower than 1 is zoom in, while higher than one is zoom out.
Crops the input tensors to a smaller version, and replicate pad the lost region. Do not turn this on, this is just for testing purpose.
Apply 3D Gaussian blur. Its low bound and high bound refers to the range of sigma of the blur. While its value is the kernel size, higher than 3 could slow down augmentation process significantly.
Self Explanatory.
Simulate lower imaging resolution by down-sampling using nearest-neighbor interpolation and then up-sampling using cubic interpolation.
Apply gamma or contrast adjustment to with a gradient pattern. From Low Bound at one (random) side of the image to High Bound at the opposite side of the image.
Used to simulate uneven staining throughout the sample.
Add salt and pepper noise. The proportion of pixels affected are between Low Bound and High Bound.
Blur the Label and the contour Label a little bit. Please do not enable them as they have shown to be ineffective.