Mission
We use machine learning, big data, and implementation science to improve the quality and safety of radiotherapy treatment.
Members
Faculty
Melissa Ghafarian
Jim Lamb
Jack Neylon
Tim Ritter
Graduate Students
Yasin Abdulkadir
John Charters
Justin Hink
Dishane Luximon
Rachel Petragallo
Former Students
John Ginn Shyam Jani
Projects and Papers
AI for RT Delivery Error Detection and Mitigation
Our group is developing algoriths, tools and techniques for the automatic detection of human errors in the alignment of image-guided radiotherapy setup images. These algorithms could be used as an interlock to prevent treatment of the wrong body part, wrong patient, or with significant alignment errors (e.g. wrong vertebral body).
Development and interinstitutional validation of an automatic vertebral-body misalignment error detector for cone-beam CT-guided radiotherapy
Development and multi-institutional validation of a convolutional neural network to detect vertebral body mis-alignments in 2D x-ray setup images
Proof-of-concept study of artificial intelligence-assisted review of CBCT image guidance
Offline generator for digitally reconstructed radiographs of a commercial stereoscopic radiotherapy image-guidance system
Human Factors Research
We study how humans interact with automation and technology to improve quality, safety and efficiency of radiotherapy treatments.
Barriers and facilitators to clinical implementation of radiotherapy treatment planning automation: A survey study of medical dosimetrists
Computational Tools for RT Image Analysis
Some of the computational tools we developed for our project which we think may have interest to others. Code publically available at links below.
Feasibility of a deep-learning based anatomical region labeling tool for Cone-Beam Computed Tomography scans in radiotherapy
Offline generator for digitally reconstructed radiographs of a commercial stereoscopic radiotherapy image-guidance system
Auto-Segmentation
Our group develops and clincally validates auto-segmentation algorithms, with a focus on human-machine cohesion and clinical evaluation.
Machine-assisted interpolation algorithm for semi-automated segmentation of highly deformable organs
Human factors in the clinical implementation of deep learning-based automated contouring of pelvic organs at risk for MRI-guided radiotherapy
Open-Source Code
Anatomical Region Labeling Tool for CBCT Scans: https://github.com/dcluximon/ARL_repo
Stereoscopic DRR generator for the ExacTrac image-guidance system https://github.com/jcharters-mp/DRR
Links We Like
PyMedPhys: https://docs.pymedphys.com/en/latest/
Webmaster:Jim Lamb