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+![image](.github/CIVMBanner.png)
+# MRI Segmentation and Radiomics
+This repository contains example code from the paper in preparation on preclinical cancer imaging titled "MRI-based 
+Deep Learning Segmentation and Radiomics of Sarcoma Tumors in Mice."
+
+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**.
+
+
+This goal of this project is to create an pipeline with two facets:
+1. Automatic segmentation of soft tissue sarcoma using deep learning.
+2. Automate calculation and analysis of radiomic features within the tumor population.
+
+## Data
+The data used in this study is composed of multi-contrast MR images of soft tissue sarcoma. Tumors were imaged using 
+T2-weighted and T1-weighted sequences. These were followed by a contrast-enhanced T1-weighted acquisition.
+![image](.github/multi_contrast.png)
+
+The full dataset will soon be available on the [CIVM VoxPort page](https://civmvoxport.vm.duke.edu/voxbase/studyhome.php?studyid=617)
+
+## Segmentation
+Segmentation was performed via a U-net CNN. The network functions on patches taken from image volumes. The general 
+network structure is shown below.
+![image](.github/cnn_structure.png)
+
+Training and perfomance anlysis is done using the [Segmentation.py](Segmentation/Segmentation.py) script. The results
+ for a network trained on multi-contrast MR images with cross entropy loss is shown below.
+ 
+ ![image](.github/segmentations.png)
+
+#### Requirements
+T2-weighted images are bias corrected using N4BiasFieldCorrection in [ANTs](http://stnava.github.io/ANTs/).
+
+Network structures have been defined in [model_keras.py](Segmentation/model_keras.py). A variety of networks have 
+been tested, including those with and without skip connections as well as testing out cross entropy and dice loss 
+functions.
+
+
+
+## Radiomics
+Radiomic features were calculated within the segmentation using [PyRadiomics](https://pyradiomics.readthedocs.io/en/latest/). 
+Radiomic 
+calculation are performed using [radiomic_calculations_batch.py](Radiomics/radiomic_calculations_batch.py).
+
+The code used to process the radiomic features is found in [radiomic_classifications.py]
+(Radiomics/radiomics_classifications.py). The features are plotted and classified using SVMs 
+and NNs to determine if primary local recurrence can be predicted in this population based only on radiomic features.
+ Machine learning classifier functions lie in [radiomic_functions.py](Radiomics/radiomic_functions.py).
+ 
+Using this code, we achieved an AUC of 0.81 for predicting recurrence within these mice.
+ 
+![image](.github/classifier_results.png)
+ 
+#### Requirements
+Due to the high dimensionality of the data (321 features per tumor) feature selection is required
+. This are accomplished via [mRMR](http://home.penglab.com/proj/mRMR/), which must be added to the path before running. 
+
+## Crawler
+A crawler has been implemented to locate, pre-process, segmentent, and compute radiomic features for large 
+collections of images. The code for this located in the "Crawler" folder and can be run through the [crawler.py](Crawler/crawler.py)
+script once the base paths have been updated.