Automated

Object tracking. For each time-frame, an image mask is obtained from median filtering (user defined radius), thresholding (user defined level) and hole filling. Convex objects are split apart by distance map watershed from regional intensity maxima (user defined noise tolerance), eroded (user defined radius) and analyzed as 3D particles (assuming some overlap between objects from a frame to the next frame). Finally, division events are analyzed and accounted for to relabel objects.

Track non-dividing particles in 2D time-lapse image.

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).

Execute Nuclei Segmentation in 3D images using pixel classification with ilastik.

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.