TensorFlow tf.data.Dataset API for medical imaging
Finally, I found a method to solve my problem.
I first crop a subject's image without applying the actual crop. I only measure the slices I need to crop the volume to only the brain. I then serialize all the data set images into one TFRecord file, each training example being an image modality, original image's shape and the slices (saved as Int64 feature). 
I decode the TFRecords afterward. Each training sample are reshaped to the shape it contains in a feature. I stack all the image modalities into a stack using tf.stack() method. I crop the stack using the previously extracted slices (the crop then applies to all images in the stack). I finally get some random patches using tf.random_crop() method that allows me to randomly crop a 4-D array  (heigh, width, depth, channel).
The only thing I still haven't figured out is data augmentation. Since all this is occurring in Tensors format, I cannot use plain Python and NumPy to rotate, shear, flip a 4-D array. I would need to do it in the tf.Session(), but I would rather like to avoid this and directly input the training handle.
For the evaluation, I serialize in a TFRecords file only one test subject per file. The test subject contains all modalities too, but since there is no TensorFLow methods to extract patches in 4-D, the image is preprocessed in small patches using Scikit-Learn extract_patches() method. I serialize these patches to the TFRecords.
This way, training TFRecords is a lot smaller. I can evaluate the test data using batch prediction.
Thanks for reading and feel free to comment !
I'm a student in medical imaging. I have to construct a neural network for image segmentation. I have a data set of 285 subjects, each with 4 modalities (T1, T2, T1ce, FLAIR) + their respective segmentation ground truth. Everything is in 3D with resolution of 240x240x155 voxels (this is BraTS data set).
As we know, I cannot input the whole image on a GPU for memory reasons. I have to preprocess the images and decompose them in 3D overlapping patches (sub-volumes of 40x40x40) which I do with scikit-image view_as_windows and then serialize the windows in a TFRecords file. Since each patch overlaps of 10 voxels in each direction, these sums to 5,292 patches per volume. The problem is, with only 1 modality, I get sizes of 800 GB per TFRecords file. Plus, I have to compute their respective segmentation weight map and store it as patches too. Segmentation is also stored as patches in the same file. 
And I eventually have to include all the other modalities, which would take nothing less than terabytes of storage. I also have to remember I must also sample equivalent number of patches between background and foreground (class balancing).
So, I guess I have to do all preprocessing steps on-the-fly, just before every training step (while hoping not to slow down training too). I cannot use tf.data.Dataset.from_tensors() since I cannot load everything in RAM. I cannot use tf.data.Dataset.from_tfrecords() since preprocessing the whole thing before takes a lot of storage and I will eventually run out.
The question is : what's left for me for doing this cleanly with the possibility to reload the model after training for image inference ?
Thank you very much and feel free to ask for any other details.
Pierre-Luc