pyCERR-Notebooks

This repository provides notebooks for various use cases of pyCERR. These include

• Organizing longitudinal, multi-modal image metadata
• IBSI-1 and IBSI-2 compatible radiomics
• RTDOSE dosimetry / DVHfeatures
• Infering image segmentation, registration using AI models

Notebooks by topic

Notebooks are grouped into numbered topic folders (roughly beginner to advanced). Each can be opened directly in Google Colab via the badge at its top.

Shared inputs live at the repo root: settings/, radiomics_settings/, radiomics_features/.

Getting started — load & visualize

01_getting_started/

Notebook	Description
pycerr_101.ipynb	Start here: the PlanC data model — load a scan, build structures & dose from arrays, get masks, visualize
structure_operations.ipynb	Structure masks, boolean ops, margins (GTV→PTV), label maps, Dice/Hausdorff comparison
desktop_viewer_quickstart.ipynb	Launch & tour the Qt desktop viewer (navigation, contouring, fusion, 3D, drag-drop, export)
load_visualize_scan_seg_ex1.ipynb	Load DICOM and view scan + segmentation
batch_visualize_scan_seg_ex1.ipynb	Visualize scan + segmentation over a batch
animate_viewer.ipynb	Animate / scroll through the viewer
copy_seg_to_scan.ipynb	Copy a segmentation between scans

Data import (DICOM / NIfTI / XNAT)

02_data_import/

Notebook	Description
export_to_nifti_and_dicom.ipynb	Import & export: scan/dose/structures → NIfTI, structures → DICOM RTSTRUCT
download_data_from_xnat.ipynb	Pull data from an XNAT server
xnat2pycerr.ipynb	XNAT → pyCERR planC
xnat2pycerr_20251020.ipynb	XNAT pull + SMIT rectal segmentation

Auto-segmentation (deep-learning models)

03_autosegmentation/

Notebook	Description
ai_seg_inference_ex1.ipynb	Generic AI segmentation inference template
ai_seg_adc_inference_ex1.ipynb	AI segmentation on ADC (MR) images
autosegment_patient_outline.ipynb	Patient outline (body) segmentation
autosegment_CT_HeadAndNeck_OARs.ipynb	CT head & neck OARs (DeepLab)
autosegment_CT_Heart_OARs.ipynb	CT heart sub-structure OARs (DeepLab)
autosegment_CT_Lung_OARs.ipynb	CT lung OARs (DeepLab)
autosegment_MR_Prostate_OARs.ipynb	MR prostate OARs
autosegment_CT_HN_SMIT.ipynb	CT head & neck (SMIT)
autosegment_CT_HeartSubStruct_SMIT.ipynb	CT heart sub-structures (SMIT)
autosegment_CT_Lung_OARs_SMIT.ipynb	CT lung OARs (SMIT)
autosegment_CT_Lung_GTV_SMIT.ipynb	CT lung GTV (SMIT)
autosegment_MR_Rectum_GTV_SMIT.ipynb	MR rectum GTV (SMIT)
MR_HN_Nodule_SMIT_demo.ipynb	MR head & neck nodule (SMIT) demo
autosegment_installer_CT_Heart_OARs.ipynb	CT heart OARs via model_installer
SBG_autosegment_CT_Heart_OARs.ipynb	CT heart OARs on the Seven Bridges platform

Image registration & QA

04_registration/

Notebook	Description
deformable_image_registration_using_ANTS.ipynb	Deformable registration with ANTs
auto_register_segment_MR_Pancreas_OARs.ipynb	Register then segment MR pancreas OARs
TG211_metrics.ipynb	AAPM TG-211 registration-QA metrics

Image preprocessing & filtering

05_preprocessing/

Notebook	Description
n4_bias_field_correct.ipynb	N4 bias-field correction
image_filters_lung_ct.ipynb	IBSI-2 image filters / texture maps

Radiomics — feature extraction

06_radiomics_extraction/

Notebook	Description
extractRadiomics.ipynb	Extract radiomics from a single dataset
batch_extract_radiomics_ex1.ipynb	Batch radiomics extraction
batch_extract_radiomics_lung_ct.ipynb	Batch radiomics extraction (lung CT)
analyzing_image_texture_using_pycerr.ipynb	Texture-feature analysis

Radiomics — validation & comparison

07_radiomics_validation/

Notebook	Description
test_ibsi2_compatibility.ipynb	IBSI-2 reference compliance check
compare_pycerr-pyrad.ipynb	Compare pyCERR vs pyradiomics

Radiomics — analysis & modeling

08_radiomics_analysis/

Notebook	Description
Radiomics_network_based_clustering.ipynb	Network-based K-means clustering
EBIC_graphical_lasso.ipynb	Graphical-lasso network model (EBIC)

Functional / quantitative imaging

09_functional_imaging/

Notebook	Description
extract_semi-quantitaive_dce-mri_features.ipynb	Semi-quantitative DCE-MRI features

Outcomes modeling (dosimetric)

10_outcomes_modeling/

Notebook	Description
normal_tissue_complication_probability.ipynb	NTCP modeling

Treatment planning (IMRT)

11_treatment_planning/

Notebook	Description
imrtp_optimization_example.ipynb	IMRT beamlet fluence-map optimization (pyCERR port of CERR's runOptimExample)
pyradplan_bridge_demo.ipynb	pyRadPlan bridge: photon/proton dose calculation and fluence optimization from planC
portpy_bridge_demo.ipynb	PortPy bridge: export an influence matrix (pyRadPlan or native QIB) and optimize with clinical criteria
bridges_realworld_dicom.ipynb	End-to-end template on your own DICOM: pick the pyRadPlan or QIB engine, set constraints, optimize, compare DVH

Dose analysis

12_dose_analysis/

Notebook	Description
dvh_and_dose_metrics.ipynb	Dose-volume histograms and dose metrics (Dx, Vx, mean/max, EUD)