OAR contouring error detection in radiation therapy

One of the most critical steps in radiation therapy treatment is accurate tumor and critical organ-at-risk (OAR) contouring. Both manual and automated contouring processes are prone to errors and to a large degree of inter- and intra-observer variability.  These are often due to the limitations of imaging techniques in visualizing human anatomy as well as to inherent anatomical variability among individuals. Physicians/physicists have to re-verify all the radiation therapy contours of every patient before using them for treatment planning, which is tedious, laborious, and still not an error-free process. In this study, we developed a general strategy based on novel geometric attribute distribution (GAD) models to automatically detect radiation therapy OAR contouring errors and facilitate the current clinical workflow.


Flowchart of the proposed contouring error detection strategy.


Visualization of automated detection results for local shape errors; (a)–(c) evaluation results of three brainstem contours; (d)-(f) evaluation results of three left parotid contours. The contours were triangulated and displayed with colored fitting-error maps. All the slices were rendered on the triangulated meshes. The incorrect slices detected by the automated algorithm were displayed in black color and pointed out by colored arrows.

Related publications:

1.  Hsin-Chen Chen, Jun Tan, Steven Dolly, James Kavanaugh, Mark A. Anastasio, Daniel A. Low, H. Harold Li, Michael Altman, Hiram Gay, Wade Thorstad, Sasa Mutic, Hua Li*, “Automated Contouring Error Detection Based on Supervised Geometric Attribute Distribution Models for Radiation Therapy: A General Strategy”, Medical Physics, 2015, Vol. 42, No. 2, pp:1048-59.

2. Michael Altman, James Kavanaugh, Omar Wooten, Green Olga, Todd DeWees, Hiram Gay, Wade Thorstad, Hua Li, Sasa Mutic, “A framework for automated contour quality assurance in radiation therapy including adaptive techniques”, Physics in Medicine and Biology, 2015, Vol. 60, pp:5199–5209.

3. Hsin-Chen Chen, James Kavanaugh, Jun Tan, Steven Dolly, Hiram Gay, Wade Thorstad, Mark Anastasio, Michael Altman, Sasa Mutic, Hua Li*, “A Supervised Framework for Automatic Contour Assessment for Radiotherapy Planning of Head-Neck Cancer”, AAPM Annual Meeting, 2014. (Best in Physics)

4. Hsin-Chen Chen, Jun Tan, James Kavanaugh, Steven Dolly, Hiram Gay, Wade Thorstad, Mark Anastasio, Michael Altman, Sasa Mutic, Hua Li*, “An Integrated Contour Evaluation Software Tool Using Supervised Pattern Recognition for Radiotherapy”, AAPM Annual Meeting, 2014.