This data is from the HVSMR 2016: MICCAI Workshop on Whole-Heart and Great Vessel Segmentation from 3D Cardiovascular MRI in Congenital Heart Disease challenge.
This segmenting command-line tools is based on transfer learning from a Dense VNet application in NiftyNet trained on CT data for abdominal organ segmentation.
If you want to make any changes or improvements to the config file, you can use the NiftyNet config file docs, or the complete docs. You can also find the source code for NiftyNet on GitHub along with other sample config files to use.
First, clone this repo using:
>>> git clone https://github.com/scouvreur/WholeHeartMRISegmenter.git
Then, do:
>>> cd WholeHeartMRISegmenter/
>>> conda create -n niftynet-dev
>>> conda activate niftynet-dev
>>> conda install tensorflow-gpu==1.12
>>> pip install niftynet opencv-python scikit-image simpleitk pyyaml
Label | Axial | Coronal | Sagittal | Classes |
---|---|---|---|---|
n/a |
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num_classes=1 |
A |
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num_classes=3 |
B |
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num_classes=5 |
Label | 3D View | ITKSnap Labels | Anatomical Explanation |
---|---|---|---|
A |
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|
B |
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To start training, within the folder use:
>>> net_segment train -c config.ini
It is recommended to use around max_iter = 2000
to reach good results.
To perform inference on the data in testing/
, you can use:
>>> net_segment inference -c config.ini
You can select the iteration checkpoint from training to perform inference on using inference_iter
. Outputted segmentations will be placed in the directory specified in save_seg_dir
, with the original name and the posfix specified in output_postfix
.