Table of Contents
The aim of this project is to write an ai that classifies brain metastases based on their primary cancers.
The following dependencies, libraries and ressources were used: HD-BET Tensorflow ...
- Train AIs (2D)
- Explore results
- Acquire patient data
- Find correct sequences for each patient
- Preprocessing
- Extract dicom metadata
- Convert dicom to nifti
- Extract brain
- extract patients brain using HD-BET
- compare HD-BET images with synthstrip images (chose HD-BET)
- Fill holes
- Binary Segment
- Cropy images
- Bias correction
- Coregister images
- Resample images
- Z-score normalization
- Merge images
- redo preprocessing using the brats-toolkit
- redo preprocessing with n4 bias correction (last time, I swear)
- segmentation
- redo segmentation on n4 bias corrected files
- manually adjust segmentation
- Build AIs
- 3D CNN (entire brain) -> unfortunately unsuccessfull :/
- Transfer ResNeXt architecture to 3D
- custom scheduler
- custom ai architecture (input: images, age, sex)
- 2D CNNs
- 2D CNN (only metastasis cutout)
- 2D CNN* (transfer learning)
- Vision Transformer (pretrained)
- 3D CNN (entire brain) -> unfortunately unsuccessfull :/