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+# Preprocessing
+
+The goal of these steps is to end up with a collection of images that are neural-network ready, and each have associated measurements (e.g. size and variance) that can be used in a structural causal model
+
+1. Download the data from the official repository http://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX
+
+   Note that in order for the package pylidc to work, it needs to know where the original data are stored; See https://pylidc.github.io/install.html for instructions
+
+2. Run lidc-preprocessing.py
+
+   This step extracts individual nodules from the ct scans and generates the 2D images from the 3D nodules. On my machine (12 threads) this takes about (nodules = 5/10 mins)
+
+3. measure_slices.py
+
+   Measure size (area) and variance of the pixel intensities based on the segmentations.
+   These measurements will form the basis of the simulations
+
+4. preprare-data-2d.py
+
+   Split the data in train/valid, move to new folder, filter out slices that are too small (<20mm) or that the annotators dont agree on and normalize measurements to approximately normal distributions
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