- 3D U-Net Based Brain Tumor Segmentation and Survival Days Prediction, 2019, Wang et al. -> crap
- Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features
- They ensembled 6 3DUNets with different numbers of layers, and they split the data 6:4, hence I do the same splits
Residual UNet paper https://arxiv.org/pdf/1909.12901v2.pdf https://www.frontiersin.org/articles/10.3389/fncom.2020.00025/full Attention U-Net: Learning Where to Look for the Pancreas (2D Unet with sophisticated attention) Brain Tumor Segmentation and Survival Prediction using 3D Attention UNet (Trivial Channel Attention on 3D UNet)
- BraTS 2020 (Test + Validation sets)
- Multi-modal scans available as NIfTI images .nii
- Four channels of information - four different volumes of the same image
- T1/Native
- T1CE/ post-contrast T1-weighted (same as first one but contrasted)
- T2 Weighted
- T2 Fluid attenuated inversion recovery volumes/ FLAIR
- Labels/Annotations
- 0: unlabeled volume: the background and parts of the brain which is normal
- 1: Necrotic and Non-enhancing tumor core (NCR/NET)
- 2: Peritumoral Edema (ED)
- 4: GD-enhancing tumor (ET)
Dataset stuff:
- Download dataset and unzip + install nibabel (Shayan)
- (FIX) Rename W39_1998.09.19_Segm -> BraTS20_Training_355_seg (Shayan)
- MinMax Scaler + Combine all volumes except for T1 native as T1 Native is the same as T1CE with worse contrast (Shayan)
- label 4 -> 3 (Shayan)
- Crop images and remove most of the black section (Shayan)
- (Extra) Drop volumes where there's not much annotation?? (Did not do this as there's not many images, to just lose one!)
- Dice Coefficient
- Accuracy
- AUC-ROC
- ...
For segmentation, variations of 3D Unet is being used, namely 3DUNet (Concatenative skips), Residual 3DUNet (Additive skips), Attention 3DUNet
