Here is the repository for pre-processing of some medical imaging data.
Working on Attention-Gated-Networks from https://github.com/ozan-oktay/Attention-Gated-Networks, you might want to run the algorithm on the original datasets. One of them is PANCREAS CT-82 which can be found from https://wiki.cancerimagingarchive.net/display/Public/Pancreas-CT.
The .dcm files store 2D slices. This code combines (and normalize) the slices of each patient into 3D volume. Each patient is coded as PANCREAS_0001, PANCREAS_0002 etc.
While working on medical images, for example in NIFTI formats, we might face memory problem. This is because a NIFTI volume might come in large sizes, for example 192x192x19 with many modalities. With large convolutional neural network, feeding the entire volume may result in out of memory error (at least my measly 4GB RAM does. Multi-view sampling is the way out of this. Using multi-view sampling, slices of the images (green rectangles) are sampled. The “multi” part of the multi-view can take the form of larger slice (red rectangles).