DHAS: A Python repository from mzza

The github repo is the code of paper "Deep Hierarchy-Aware Segmentation: A Novel Framework for MRIs Brain Tumor Segmentation" submitted to IEEE Transactions on Medical Imaging

If you face any problem, please feel free to open an issue.

Directory structure

.
├─ data
  ├─ brats20	# Data provided by the BraTS 2020 competition host
    ├─ TrainingData
	├─ BraTS20_Training_001
	    ├─ BraTS20_Training_001_flair.nii.gz
	    ├─ BraTS20_Training_001_seg.nii.gz
	    ├─ BraTS20_Training_001_t1.nii.gz
	    ├─ BraTS20_Training_001_t1ce.nii.gz
	    ├─ BraTS20_Training_001_t2.nii.gz
	├─ BraTS20_Training_002
	├─ ...
    ├─ ValidationData
	├─ BraTS20_Validation_001
	    ├─ BraTS20_Validation_001_flair.nii.gz
	    ├─ BraTS20_Validation_001_t1.nii.gz
	    ├─ BraTS20_Validation_001_t1ce.nii.gz
	    ├─ BraTS20_Validation_001_t2.nii.gz
	    ├─ ...
├─ model	# Generated training and validation split (training_ids.pkl, validation_ids_pkl, test_ids.pkl), processed data file (brats20_data.h5, brats20_data_test.h5), and save best training model (isensee_2017_model.h5)
├─ output	# Generated prediction file
├─ src		# Souce code
    ├─ unet3d
    ├─ config.py
    ├─ inference.py
    ├─ train.py
├─ inference.sh
├─ train.sh

Packages

Python >= 3.5 (my current version is 3.7.7)
tensorflowgpu==1.15 (other 1.x version should work)
Other packages: pytables, SimpleITK, nilearn, nibabel
Optional package: nipype (For n4itk bias correction preprocessing only. However, I didn't achieve that much performance gain using this technique!)

Note for installing 'pytables': install it using conda ('conda install pytables'). Installing using pip ('pip install tables') raises 'memory dump' issue!

How to run

If you prepare directory structure properly, you are done!

Train:

$ ./train.sh

Validation/Test:

$ ./inference.sh

Acknowledgment