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2. Automate calculation and analysis of radiomic features within the tumor population.
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2. Automate calculation and analysis of radiomic features within the tumor population.
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## Data

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## Data





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The data used in this study is composed of multi-contrast MR images of soft tissue sarcoma. Tumors were imaged using 

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The data used in this study is composed of multi-contrast MR images of soft tissue sarcoma. Tumors were imaged using 





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T2-weighted and T1-weighted sequences. These were followed by a contrast-enhanced T1-weighted acquisition.

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T2-weighted and T1-weighted sequences. These were followed by a contrast-enhanced T1-weighted acquisition.





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![image](https://github.com/mdholbrook/MRI_Segmentation_Radiomics/blob/master/.github/multi_contrast.png)

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![image](https://github.com/mdholbrook/MRI_Segmentation_Radiomics/blob/master/.github/multi_contrast.png?raw=true)





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The full dataset will soon be available on the [CIVM VoxPort page](https://civmvoxport.vm.duke.edu/voxbase/studyhome.php?studyid=617)

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The full dataset will soon be available on the [CIVM VoxPort page](https://civmvoxport.vm.duke.edu/voxbase/studyhome.php?studyid=617)





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## Segmentation

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## Segmentation





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Segmentation was performed via a U-net CNN. The network functions on patches taken from image volumes. The general 

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Segmentation was performed via a U-net CNN. The network functions on patches taken from image volumes. The general