Particle tracking in 2D time-lapse based on linking closest regional intensity minima (user defined noise tolerance) detected from Laplacian of Gaussian filtered images (user defined radius). A maximum linking distance is set (user defined).
The macro will segment nuclei and separate clustered nuclei in a 3D image using a 2D Gaussian blur, followed by Thresholding, 2D hole filling and a 2D watershed. As a result an index-mask image is written for each input image.
U-Net segmentation as presented in Reference Publication. The model predicts three classes: background, edge and foreground. The model was trained with Kaggle Data Science Bowl (DSB) 2018 training set.
