Putting great tools in the hand of clinicians. https://github.com/MLMedic/MLMedic
This is the project repository for the Brisbane health hack 2019. The goal is to develop an interface for applying mashine learning models to medical imaging data.
- platform-independent GUI in Python / Electron / ?
- Import of Dicom data
- Applying Machine Learning models to this dicom data (example: Segmentation and Highlighting of Brain Lesions)
- Visualising Output
- Model zoo online with upload possibility
- Model conversion from Tensorflow, PyTorch, Caffe, Theano .... to be able to be used in our GUI
- Local Transfer Learning to adjust models to available data at the local site
- 3T and 7T MPRAGE and MP2RAGE anatomical scans
- dicom and nii format
- link via email
- https://github.com/DLTK/models/tree/master/ukbb_neuronet_brain_segmentation (Tensorflow)
- https://github.com/zsdonghao/u-net-brain-tumor.git (TensorFlow)
- https://pypi.org/project/NiftyNet/ (Nifty net - works very easily)
- https://github.com/josedolz/LiviaNET (Python 2 and Theano)
- https://github.com/Entodi/MeshNet (Torch)
- http://64.234.162.248:3000/about (Same as above)
https://github.com/DLTK/models/tree/master/ukbb_neuronet_brain_segmentation
- install miniconda https://conda.io/miniconda.html or anaconda
- wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
- bash Miniconda3-latest-Linux-x86_64.sh
- conda install tensorflow
- pip install dltk
- clone model repo:
- git clone https://github.com/DLTK/models
- download Models:
- wget http://www.doc.ic.ac.uk/~mrajchl/dltk_models/model_zoo/neuronet/spm_tissue.tar.gz
- tar -xzf spm_tissue.tar.gz (into /models/ukbb_neuronet_brain_segmentation/
- copy files from spm_tissue/0/1513180449 up one level to spm_tissue/0
- adjust paths in /models/ukbb_neuronet_brain_segmentation/config_spm_tissue.json so they point to the path /models/ukbb_neuronet_brain_segmentation/spm_tissue or whatever is relevant.
- create and add this to /models/ukbb_neuronet_brain_segmentation/files.csv in two lines: id,t1,fsl_fast,fsl_first,spm_tissue,malp_em,malp_em_tissue 5404127,3T.nii.gz,T1_brain_seg.nii.gz,all_fast_firstseg.nii.gz,T1_brain_seg_spm.nii.gz,T1_MALPEM.nii.gz,T1_MALPEM_tissues.nii.gz
- download 3T file from link provided on owncloud and name it 3T.nii.gz, place it in /models/ukbb_neuronet_brain_segmentation/
- run the model!
- python deploy.py --csv files.csv --config config_spm_tissue.json
From https://github.com/Entodi/MeshNet
- First you need Torch!
- Steps taken from https://dwijaybane.wordpress.com/2017/07/22/installing-torch-7-deep-learning-on-ubuntu-16-04/
- sudo apt-get install --no-install-recommends git software-properties-common
- export TORCH_ROOT=~/torch
- git clone https://github.com/torch/distro.git $TORCH_ROOT --recursive
- cd $TORCH_ROOT
- ./install-deps
- ./install.sh -b
- Now download the models for MeshNet AKA BrainChop
- git clone https://github.com/Entodi/MeshNet.git
- Download the 3T data from owncloud link
- Install python and dependencies if you haven't:
- pip install nipy
- Conform T1 to 1mm voxel size in coronal slice direction with side length 256.
- (Freesurfer required) mri_convert brainDir/t1.nii brainDir/t1_c.nii -c
- Convert nifti to numpy format
- python nifti2npy.py brainDir/t1_c.nii --npy_file brainDir/T1.npy
- Create segmentation using predict.lua providing path to directory with brain npy file brainDir
- th predict.lua -modelFile ./saved_models/model_Mon_Jul_10_16:43:55_2017/model_219.t7 -brainPath brainDir
- Convert numpy segmentation file to nifti format by providing base nifti file
- python npy2nifti.py segmentation.npy t1_c.nii