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[P] Framework for medical image segmentation with tools and models off-the-shelf

Hi all,

At my university, I am taking a grad course in which we’re select a deep learning project and work on it by the end of the course.I had previously developed a set of tools for my M.Sc project where I need to manipulate sets of medical images in different formats (DICOM, Niftii, Nrrd), pre-process them using SimpleITK and feed them into a deep learning pipeline.I figured I could execute that course project using those tools and put it up on github for everyone to use and for me to showcase my skills, as I will finish school very soon and that will give me some visibility for my upcoming job search.

The platform relies on visdom for visualization capabilities, I had implemented functions that allow you to visualize your network’s computation graph, the experiment options and hyperparameters, your loss and accuracy curves, the gradient flow graphs through your networks as well as histograms for weight distribution in your layers. The python classes offer different functions to easily extend functionality where you can sample images during training if you’re working with images and display them on the browser.

The repository comes with implementations of UNets and ResNets as well as many GAN loss functions such as Wasserstein GAN with gradient penalty. The same class can also be set in non-GAN mode so that it uses a simple cross-entropy loss function.

I had used this code to segment vertebrae from MRIs, reaching a 0.87 dice coefficient.

The code is generic enough to be used for tasks other than image segmentation if you wish to play around with it.

I hope this will be helpful to you.

Please find it in the link down below:

Here is an example of segmentation on unseen data:

submitted by /u/DifficultDifficulty
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