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![image](https://github.com/mdholbrook/MRI_Segmentation_Radiomics/blob/master/.github/CIVMBanner.png)
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![image](https://github.com/mdholbrook/MRI_Segmentation_Radiomics/blob/master/.github/CIVMBanner.png?raw=true)
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# MRI Segmentation and Radiomics
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# MRI Segmentation and Radiomics
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This repository contains example code from the paper in preparation on preclinical cancer imaging titled "MRI-based
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This repository contains example code from the paper in preparation on preclinical cancer imaging titled "MRI-based
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Deep Learning Segmentation and Radiomics of Sarcoma Tumors in Mice."
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Deep Learning Segmentation and Radiomics of Sarcoma Tumors in Mice."
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This work is part of the [U24 co-clinical trial](https://sites.duke.edu/pcqiba/) of which [CIVM](http://www.civm.duhs.duke.edu/) at Duke University is a participant. This work has been funded by **NIH U24CA220245**.

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This work is part of the [U24 co-clinical trial](https://sites.duke.edu/pcqiba/) of which [CIVM](http://www.civm.duhs.duke.edu/) at Duke University is a participant. This work has been funded by **NIH U24CA220245**.





  
  
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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 





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network structure is shown below.

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network structure is shown below.





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

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





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Training and perfomance anlysis is done using the [Segmentation.py](Segmentation/Segmentation.py) script. The results

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Training and perfomance anlysis is done using the [Segmentation.py](Segmentation/Segmentation.py) script. The results





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 for a network trained on multi-contrast MR images with cross entropy loss is shown below.

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 for a network trained on multi-contrast MR images with cross entropy loss is shown below.





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

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





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#### Requirements

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#### Requirements





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T2-weighted images are bias corrected using N4BiasFieldCorrection in [ANTs](http://stnava.github.io/ANTs/).

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T2-weighted images are bias corrected using N4BiasFieldCorrection in [ANTs](http://stnava.github.io/ANTs/).





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Network structures have been defined in [model_keras.py](Segmentation/model_keras.py). A variety of networks have 

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Network structures have been defined in [model_keras.py](Segmentation/model_keras.py). A variety of networks have 





  
  
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and NNs to determine if primary local recurrence can be predicted in this population based only on radiomic features.

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and NNs to determine if primary local recurrence can be predicted in this population based only on radiomic features.





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 Machine learning classifier functions lie in [radiomic_functions.py](Radiomics/radiomic_functions.py).

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 Machine learning classifier functions lie in [radiomic_functions.py](Radiomics/radiomic_functions.py).





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Using this code, we achieved an AUC of 0.81 for predicting recurrence within these mice.

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Using this code, we achieved an AUC of 0.81 for predicting recurrence within these mice.





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

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





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#### Requirements

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#### Requirements





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Due to the high dimensionality of the data (321 features per tumor) feature selection is required

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Due to the high dimensionality of the data (321 features per tumor) feature selection is required





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. This are accomplished via [mRMR](http://home.penglab.com/proj/mRMR/), which must be added to the path before running. 

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. This are accomplished via [mRMR](http://home.penglab.com/proj/mRMR/), which must be added to the path before running. 





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## Crawler

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## Crawler





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A crawler has been implemented to locate, pre-process, segmentent, and compute radiomic features for large 

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A crawler has been implemented to locate, pre-process, segmentent, and compute radiomic features for large 





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collections of images. The code for this located in the "Crawler" folder and can be run through the [crawler.py](Crawler/crawler.py)

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collections of images. The code for this located in the "Crawler" folder and can be run through the [crawler.py](Crawler/crawler.py)





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script once the base paths have been updated.

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script once the base paths have been updated.