Projects

This page summarizes active and legacy open-source projects from Choi Lab. Active qradiomics Unified Python radiomics CLI and library for end-to-end workflows (DICOM conversion, feature extraction, modeling, and reproducible orchestration). Repo: github.com/choilab-jefferson/qradiomics License: MIT Status: actively maintained Legacy (superseded by qradiomics) Lung Cancer Screening Radiomics Earlier end-to-end screening workflow on LIDC-IDRI and LUNGx with AutoML components. Repo: github.com/choilab-jefferson/LungCancerScreeningRadiomics Status: no longer maintained Radiomics Tools C++/Python (ITK, Ruffus) radiomics pipeline including DICOM/RT handling and feature extraction. ...

License: MIT · Python: 3.11+ · Version: 0.9.0 · Repo: choilab-jefferson/qradiomics Active successor for three earlier Choi Lab radiomics codebases. The C++/MATLAB pipelines in taznux/radiomics-tools, taznux/lung-image-analysis, and choilab-jefferson/LungCancerScreeningRadiomics are superseded by this repo. The feature extractors are now in qradiomics.feature.rtools (Python ITK port, numerically exact to the C++ binary). New work should land here. Radiomics research CLI. qr does two things equally well: Atomic tasks — convert DICOM, extract features, merge clinical, fit a model. Each is a single command, files in / files out. Workflow assembly — generate, mutate, scaffold, and run multi-step pipelines from those atomic tasks. Default executor is Nextflow (per-patient parallel + cache + HPC); Prefect is the secondary executor; inline is the small-cohort fallback. The canonical radiomics data flow has four stages — data → image → features → modeling — and one qr workflow plan call instantiates the whole chain: ...

Interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma Pathway image: Grid structure conversion for biological array data (a non-grid structured format) for CNNs. Interpretation of the CNN model using GradCAM. Source code: https://github.com/mskspi/PathCNN Jung Hun Oh, Wookjin Choi, Euiseong Ko, Mingon Kang, Allen Tannenbaum, Joseph O Deasy, PathCNN: interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma, Bioinformatics, Volume 37, Issue Supplement_1, July 2021, Pages i443–i450, https://doi.org/10.1093/bioinformatics/btab285 ...

⚠️ This project is no longer maintained. It has been superseded by qradiomics, which provides a unified modern Python implementation. A comprehensive framework for lung cancer screening radiomics using LIDC-IDRI and LUNGx dataset. Data preprocessing - download data, conversion, etc. Radiomics feature extraction including spiculation features AutoML model building and validation Source code https://github.com/taznux/LungCancerScreeningRadiomics Wookjin Choi, Jung Hun Oh, Sadegh Riyahi, Chia-Ju Liu, Feng Jiang, Wengen Chen, Charles White, Andreas Rimner, James G. Mechalakos, Joseph O. Deasy, and Wei Lu, “Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer”, Medical Physics, Vol. 45, No. 4, pp. 1537-1549, April 2018. https://doi.org/10.1002/mp.12820 Wookjin Choi, Saad Nadeem, Sadegh Riyahi, Joseph O. Deasy, Allen Tannenbaum, Wei Lu, “Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening.” Computer methods and programs in biomedicine. 200 2021. https://doi.org/10.1016/j.cmpb.2020.105839

⚠️ This project is no longer maintained. It has been superseded by qradiomics, which provides a unified modern Python implementation. A basic framework for pulmonary nodule detection and characterization in CT https://github.com/taznux/lung-image-analysis Tested on LIDC-IDRI dataset (https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI) LIDC XML parsing Simple lung segmentation, nodule detection, and feature extraction algorithms Evaluation of nodule segmentation, detection, and characterization by LIDC XML annotations written in Matlab by Wookjin Choi and Ji-Seok Yoon This framework is the essential parts of the following papers. ...

⚠️ This project is no longer maintained. It has been superseded by qradiomics, which provides a unified modern Python implementation. Image processing tools and ruffus based pipeline for radiomics feature analysis Github repository https://github.com/taznux/radiomics-tools/ First release 0.1 with OSX binary https://github.com/taznux/radiomics-tools/releases/tag/release/0.1 Test dataset - https://wiki.cancerimagingarchive.net/display/Public/SPIE-AAPM+Lung+CT+Challenge Publications Wookjin Choi, Jung Hun Oh, Sadegh Riyahi, Chia-Ju Liu, Feng Jiang, Wengen Chen, Charles White, Andreas Rimner, James G. Mechalakos, Joseph O. Deasy, and Wei Lu, “Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer”, Medical Physics, Vol. 45, No. 4, pp. 1537-1549, April 2018. https://doi.org/10.1002/mp.12820 Wookjin Choi, Sadegh Riyahi, Seth J. Kligerman, Chia-Ju Liu, James G. Mechalakos and Wei Lu, “Technical Note: Identification of Normal Lung CT Texture Features Robust to Tumor Size for the Prediction of Radiation-Induced Lung Disease”, International Journal of Medical Physics, Clinical Engineering and Radiation Oncology, Vol.7 No.3, Paper ID 86485, pp. 330-338, August 2018. doi:10.4236/ijmpcero.2018.73027 Sadegh Riyahi, Wookjin Choi, Chia-Ju Liu, Hualiang Zhong, Abraham J Wu, James G Mechalakos, Wei Lu, “Quantifying local tumor morphological changes with Jacobian map for prediction of pathologic tumor response to chemo-radiotherapy in locally advanced esophageal cancer”, Physics in Medicine and Biology, Vol. 63, No. 14, 145020 (13pp), July 2018. doi:10.1088/1361-6560/aacd22

⚠️ This project is no longer maintained. It has been superseded by qradiomics, which provides a unified modern Python implementation. https://github.com/taznux/qualia QuaLIA (Quantitative Lung Image Analysis) Open-source framework for Computer-Aided Detection/Diagnosis OSX/Java/ITK/VTK/Gradle QuaLIA CAD Download Source code Download Binary