Gliomas are Brain tumors that involve glial cells in the brain or spinal cord. Gliomas are classified as grades I to IV, where the grades indicate severity. The grades include grade 1 (benign, curable with complete surgical resection), grade II (low grade, undergo surgical resection, radiotherapy/chemotherapy, grade III/IV (high-grade glioma’s), and grade IV (glioblastoma). The task is to identify the location of the tumor, and its classification into three groups; edema (indicates inflammation), enhancing (indicates part of the tumor with active growth), and the necrotic core (dead tissue, generally in the center). This task is important in practice, as the results are used for surgical planning.

Each sample is a tensor of size = 4 x height (H) x width (W) x depth (D), where the four 3D tensors represent the a) native (T1) and b) post-contrast T1-weighted (T1Gd), c) T2-weighted (T2), and d) T2-FLAIR contrasts (images) of the brain MRI. Note that different samples may have slightly different dimensions H, W, and D -- your are models should be able to handle this. For training samples, you are also provided with a HxWxD label “image”, with a label for each voxel (3D pixel). Your task is to predict the segmentation of unlabelled images. This is also known as semantic segmentation in computer vision. Beyond the course notes, you can find some straightforward descriptions here, and here Note that the output dimension for each sample is of size H x W x D.