brats/mic-dkfz repository overview

This repository is hosted by the BraTS organizers with permission by the original author. Input format: b17-gz-f

Docker container with the inference code for Isensee et al. (BraTS 2018 submission)

In this container is only the network that was cotrained with BraTS2018+MedicalDecathlon data. We cannot publish the parameters that resulted from cotraining with our institutional data.

Run it like this: nvidia-docker run -v PATH_TO_DATA:/data -it fabianisensee/isen2018 python3.6 predict_patient.py

Note that this docker requires just 1-2 CPU cores and a little bit of RAM (4GB should be enough, go with 8 to be sure), but a good GPU! Prediction is done fully convolutionally, so the actual amount of required GPU Memory varies with patient size. I had no problems predicting everything on a Titan X GPU with 12 GB of VRAM.

Prediction of one patient takes ~1-2 min