Image Segmentation application project for medical images using Tensorflow 2.0. This is part of my solution (benchmark) for one of the tasks of the challenge QUBIQ.
The purpose of the challenge is to benchmark segmentation algorithms returning uncertainty estimates (probability scores, variability regions, etc.) of structures in medical imaging segmentation tasks.
Training and test data comprised 7 binary segmentation tasks in four different CT and MR data sets. For this repo, I've just shown my solution for Brain growth images (MRI)'s task, which has 39 cases, 7 expert's annotations on its training set. Validation set has 5 cases and was available just for participants.
For my benchmark I used U-Net as it is a very known architecture for Image Segmentation on medical images. Focal loss also was used as it gave better results than classic loss fuctions.
As can be seen, the results were very close to the ground truth on validation set.
