Projects

License: MIT · Python: 3.11+ · Repo: choilab-jefferson/qradiomics 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 ...

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

qradiomics Open-source source for the qradiomics.com site and content. Source code: https://github.com/choilab-jefferson/qradiomics Lung Cancer Screening Radiomics A full system for lung cancer screening radiomics using Lung Image Analysis Framework, Radiomics Tools, and other external tools with Docker. PathCNN Interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma Radiomics Tools Radiomic feature analysis tools in C++ and Python with ITK, Simple ITK. Ruffus pipeline structure Image processing tools for radiomics analysis, e.g. DICOM and DICOM-RT handling, contour manipulation, automatic segmentation, etc. Interfaces for invoking MATLAB and external executables, e.g. CERR, plastimatch and etc. 3D and 2D shape, intensity, and texture (GLCM, GLRM) features ...

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. Wookjin Choi, Tae-Sun Choi, “Automated Pulmonary Nodule Detection based on Three-dimensional Shape-based Feature Descriptor”, Computer Methods and Programs in Biomedicine, Vol. 113, No. 1, January 2014, pp. 37–54, doi: http://dx.doi.org/10.1016/j.cmpb.2013.08.015 Wookjin Choi, Tae-Sun Choi, “Automated Pulmonary Nodule Detection System in Computed Tomography Images: A Hierarchical Block Classification Approach”, Entropy, Vol. 15, No. 2, pp. 507-523, February 2013, doi: http://dx.doi.org/10.3390/e15020507 Wookjin Choi, Tae-Sun Choi, “Genetic Programming-based feature transform and classification for the automatic detection of pulmonary nodules on computed tomography images”, Information Sciences, Vol. 212, pp. 57-78, December 2012, doi:http://dx.doi.org/10.1016/j.ins.2012.05.008

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

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