These pipelines are related to the following article The data used for this research are publicly available on Zenodo
Before to go to data mining and mahcine learning, there is a need to register the DCE-MRI data and the ground-truth. The registration was programed in C++ with the ITK toolbox. You need to compile the code to be able to call the executable.
Therefore, you can compile the code from the root directory as:
$ mkdir build
$ cd build
$ cmake ../src
Two executables will be created in bin/:
reg_dce: register DCE-MRI data to remove motion during the acquisition.reg_gt: register the T2W-MRI, DCE-MRI, and ground-truth data.
You can call the executable ./reg_dce as:
./reg_dce arg1 arg2
arg1is the folder with the DCE-MRI data,arg2is the storage folder with the registered DCE-MRI data.
You can call the executable ./reg_gt as:
./reg_gt arg1 arg2 arg3 arg4
arg1is the T2W-MRI ground-truth with the segmentation of the prostate.arg2is the DCE-MRI ground-truth with the segmentation of the prostate.arg3is the folder with the DCE-MRI with intra-modality motion correction (seereg_dce),arg4is the storage folder inter-modality motion correction.
The following pre-processing routines were applied:
- Standard time normalization.
Addionally, Tofts model requires an estimate of the AIF which can be derived from a population.
In the file pipeline/feature-preprocessing/pipeline_normalization_model.py, you need to set the following variables:
path_patients,path_dce,path_gt,label_gt: the appropriate data folder,path_store_model,filename_model: the path where to store the model computed.
In the file pipeline/feature-preprocessing/pipeline_normalization_patient.py, you need to set the following variables:
path_patients,path_dce,path_gt,label_gt,pt_mdl: the appropriate data folder,path_store,filename: the path where to store the normalization object computed.
In the file pipeline/feature-preprocessing/pipeline_average_aif.py and pipeline/feature-preprocessing/pipeline_average_aif_normalized.py, you need to set the following variables:
path_patients,path_dce,path_gt,label_gt: the appropriate data folder,
The variables that can be changed are:
params: parameters of the standard time normalization. Refer to the help ofprotoclass.StandardTimeNormalizationand the functionpartial_fit_modelandfit.
To normalize the DCE MRI data, launch an ipython or python prompt and run from the root directory:
>> run pipeline/feature-preprocessing/pipeline_normalization_model.py
>> run pipeline/feature-preprocessing/pipeline_normalization_patient.pyTo compute the population-based AIF, launch an ipython or python prompt and run from the root directory:
>> run pipeline/feature-preprocessing/pipeline_average_aif.py
>> run pipeline/feature-preprocessing/pipeline_average_normalized.pyThese two scripts will provide an estimate of the AIF signal which will be used later in Tofts quantification.
The following quantification methods were applied:
- Brix model,
- Hoffmann model,
- PUN model,
- Tofts model,
- Semi-quantitative model.
For each extraction method, the following variables need to be set:
path_patients,path_dce,path_gt,label_gt: the appropriate data folder,path_store: the path where to store the normalization object computed.
Additionally, for the normalized data, the following variable need to be set:
path_norm: the appropriate path to the normalization objects store in the previous section.
For the normalized data, you need to set:
shift: the variable which is the maximum offtsets found during the preprocessing.
For the scripts pipeline/feature-extraction/pipeline_tofts_extraction_population_aif***.py, you need to set:
aif_params: the parameters fo the population-based AIF found in pre-processing.
From the root directory, launch an ipython or python prompt and run:
>> run pipeline/feature-extraction/pipeline_brix_extraction_normalized.py
>> run pipeline/feature-extraction/pipeline_brix_extraction.py
>> run pipeline/feature-extraction/pipeline_hoffmann_extraction_normalized.py
>> run pipeline/feature-extraction/pipeline_hoffmann_extraction.py
>> run pipeline/feature-extraction/pipeline_pun_extraction_normalized.py
>> run pipeline/feature-extraction/pipeline_pun_extraction.py
>> run pipeline/feature-extraction/pipeline_semi_quantitative_normalized.py
>> run pipeline/feature-extraction/pipeline_semi_quantitative.py
>> run pipeline/feature-extraction/pipeline_tofts_extraction_patient_aif_normalized.py
>> run pipeline/feature-extraction/pipeline_tofts_extraction_patient_aif.py
>> run pipeline/feature-extraction/pipeline_tofts_extraction_population_aif_normalized.py
>> run pipeline/feature-extraction/pipeline_tofts_extraction_population_aif.pyThe following classification methods were applied:
- Random Forest.
For un-normalized data, you need to set the following variables:
path_patients,path_dce,path_gt,label_gt: the appropriate data folder,path_store: the path where to store the normalization object computed.
For normalized data, you need to set the following variables:
path_patients,path_dce,path_gt,label_gt,path_norm: the appropriate data folder,path_store: the path where to store the normalization object computed.
The variables that can be changed are:
config: which corresponds to the different classification methods to test.
From the root directory, launch an ipython or python prompt and run:
>> run pipeline/feature-classification/pipeline_classifier_unormalized_ese.py
>> run pipeline/feature-classification/pipeline_classifier_normalized_ese.py
>> # Brix model
>> run pipeline/feature-classification/brix/pipeline_classifier_brix_A_normalized.py
>> run pipeline/feature-classification/brix/pipeline_classifier_brix_A.py
>> run pipeline/feature-classification/brix/pipeline_classifier_brix_kel_normalized.py
>> run pipeline/feature-classification/brix/pipeline_classifier_brix_kel.py
>> run pipeline/feature-classification/brix/pipeline_classifier_brix_kep_normalized.py
>> run pipeline/feature-classification/brix/pipeline_classifier_brix_kep.py
>> run pipeline/feature-classification/brix/pipeline_classifier_brix_normalized.py
>> run pipeline/feature-classification/brix/pipeline_classifier_brix.py
>> # Hoffmann model
>> run pipeline/feature-classification/hoffmann/pipeline_classifier_hoffmann_A_normalized.py
>> run pipeline/feature-classification/hoffmann/pipeline_classifier_hoffmann_A.py
>> run pipeline/feature-classification/hoffmann/pipeline_classifier_hoffmann_kel_normalized.py
>> run pipeline/feature-classification/hoffmann/pipeline_classifier_hoffmann_kel.py
>> run pipeline/feature-classification/hoffmann/pipeline_classifier_hoffmann_kep_normalized.py
>> run pipeline/feature-classification/hoffmann/pipeline_classifier_hoffmann_kep.py
>> run pipeline/feature-classification/hoffmann/pipeline_classifier_hoffmann_normalized.py
>> run pipeline/feature-classification/hoffmann/pipeline_classifier_hoffmann.py
>> # PUN model
>> run pipeline/feature-classification/pun/pipeline_classifier_pun_a0_normalized.py
>> run pipeline/feature-classification/pun/pipeline_classifier_pun_a0.py
>> run pipeline/feature-classification/pun/pipeline_classifier_pun_beta_normalized.py
>> run pipeline/feature-classification/pun/pipeline_classifier_pun_beta.py
>> run pipeline/feature-classification/pun/pipeline_classifier_pun_normalized.py
>> run pipeline/feature-classification/pun/pipeline_classifier_pun.py
>> run pipeline/feature-classification/pun/pipeline_classifier_pun_r_normalized.py
>> run pipeline/feature-classification/pun/pipeline_classifier_pun_r.py
>> # Semi-quantitative model
>> run pipeline/feature-classification/semi/pipeline_classifier_semi_quantitative_auc_normalized.py
>> run pipeline/feature-classification/semi/pipeline_classifier_semi_quantitative_auc.py
>> run pipeline/feature-classification/semi/pipeline_classifier_semi_quantitative_normalized.py
>> run pipeline/feature-classification/semi/pipeline_classifier_semi_quantitative.py
>> run pipeline/feature-classification/semi/pipeline_classifier_semi_quantitative_rel_enh_normalized.py
>> run pipeline/feature-classification/semi/pipeline_classifier_semi_quantitative_rel_enh.py
>> run pipeline/feature-classification/semi/pipeline_classifier_semi_quantitative_tau_normalized.py
>> run pipeline/feature-classification/semi/pipeline_classifier_semi_quantitative_tau.py
>> run pipeline/feature-classification/semi/pipeline_classifier_semi_quantitative_washin_normalized.py
>> run pipeline/feature-classification/semi/pipeline_classifier_semi_quantitative_washin.py
>> run pipeline/feature-classification/semi/pipeline_classifier_semi_quantitative_washout_normalized.py
>> run pipeline/feature-classification/semi/pipeline_classifier_semi_quantitative_washout.py
>> # Tofts model
>> run pipeline/feature-classification/tofts/pipeline_classifier_tofts_ktrans_patient_aif_normalized.py
>> run pipeline/feature-classification/tofts/pipeline_classifier_tofts_ktrans_patient_aif.py
>> run pipeline/feature-classification/tofts/pipeline_classifier_tofts_ktrans_population_aif_normalized.py
>> run pipeline/feature-classification/tofts/pipeline_classifier_tofts_ktrans_population_aif.py
>> run pipeline/feature-classification/tofts/pipeline_classifier_tofts_patient_aif_normalized.py
>> run pipeline/feature-classification/tofts/pipeline_classifier_tofts_patient_aif.py
>> run pipeline/feature-classification/tofts/pipeline_classifier_tofts_population_aif_normalized.py
>> run pipeline/feature-classification/tofts/pipeline_classifier_tofts_population_aif.py
>> run pipeline/feature-classification/tofts/pipeline_classifier_tofts_ve_patient_aif_normalized.py
>> run pipeline/feature-classification/tofts/pipeline_classifier_tofts_ve_patient_aif.py
>> run pipeline/feature-classification/tofts/pipeline_classifier_tofts_ve_population_aif_normalized.py
>> run pipeline/feature-classification/tofts/pipeline_classifier_tofts_ve_population_aif.py
>> run pipeline/feature-classification/tofts/pipeline_classifier_tofts_vp_patient_aif_normalized.py
>> run pipeline/feature-classification/tofts/pipeline_classifier_tofts_vp_patient_aif.py
>> run pipeline/feature-classification/tofts/pipeline_classifier_tofts_vp_population_aif_normalized.py
>> run pipeline/feature-classification/tofts/pipeline_classifier_tofts_vp_population_aif.pyTo generate the figues in the results directory, launch an ipython or python prompt and run from the root directory:
>> run pipeline/feature-validation/feature_validation_normalisation_classifier.py
>> run pipeline/feature-validation/feature_validation_normalized_pharmaco_params.py
>> run pipeline/feature-validation/feature_validation_unormalized_pharmaco_params.py
>> run pipeline/feature-validation/feature_validation_all_methods_params.py