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--- a/README.md +++ b/README.md @@ -1,60 +1,60 @@ - -# 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. - - -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. - - -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. - -  - -#### 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. - - - -#### 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. + +# 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. + + +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. + + +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. + +  + +#### 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. + + + +#### 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.