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BraTS 2018 utilizes multi-institutional pre-operative MRI scans and focuses on the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas. Furthermore, to pinpoint the clinical relevance of this segmentation task, BraTS’18 also focuses on the prediction of patient overall survival, via integrative analyses of radiomic features and machine learning algorithms
The dataset is divided into 2 types of cancer - HGG (High-grade gliomas) and LGG (Low-grade gliomas). For every type of cancer, we will have 3D MRI scans (voxel) of patients that were acquired with different clinical protocols and various scanners from multiple (n=19) institutions. For every patient with four filters:
Each filter is an MRI scan that acts as a feature. and
The segmented MRI scenes have been segmented manually, by one to four raters, following the same annotation protocol, and their annotations were approved by experienced neuro-radiologists. The segmented data have been labeled using 3 different pixels:
Image type: NIfTI files (.nii.gz)
Dataset tree:
.
├── HGG
│ ├── Brats18_2013_10_1
│ │ ├── Brats18_2013_10_1_flair.nii.gz
│ │ ├── Brats18_2013_10_1_seg.nii.gz
│ │ ├── Brats18_2013_10_1_t1.nii.gz
│ │ ├── Brats18_2013_10_1_t1ce.nii.gz
│ │ └── Brats18_2013_10_1_t2.nii.gz
│ ├── Brats18_2013_11_1
│ │ ├── Brats18_2013_11_1_flair.nii.gz
│ │ ├── Brats18_2013_11_1_seg.nii.gz
│ │ ├── Brats18_2013_11_1_t1.nii.gz
│ │ ├── Brats18_2013_11_1_t1ce.nii.gz
│ │ └── Brats18_2013_11_1_t2.nii.gz
│ ├── ...
├── LGG
│ ├── Brats18_2013_0_1
│ │ ├── Brats18_2013_0_1_flair.nii.gz
│ │ ├── Brats18_2013_0_1_seg.nii.gz
│ │ ├── Brats18_2013_0_1_t1.nii.gz
│ │ ├── Brats18_2013_0_1_t1ce.nii.gz
│ │ └── Brats18_2013_0_1_t2.nii.gz
│ ├── Brats18_2013_15_1
│ │ ├── Brats18_2013_15_1_flair.nii.gz
│ │ ├── Brats18_2013_15_1_seg.nii.gz
│ │ ├── Brats18_2013_15_1_t1.nii.gz
│ │ ├── Brats18_2013_15_1_t1ce.nii.gz
│ │ └── Brats18_2013_15_1_t2.nii.gz
│ ├── ...
├── survival_data.csv
[ ] Create two models for the different types of tumors - HGG and LGG. [ ] Change the classification from multi-class (ncr,ed,et) to mask (one channel). [ ] Use 2D U-net model. [ ] Change the image size to the paper's size (4,160,192,128) with a batch size of 1. [ ] Use different model architecture. [ ] Change the normalization of the images. [ ] Add global const file and change the stage const [ ] Split and save to csv by names of patients
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