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Categorizing segmentation quality using a quantitative quality assurance algorithm
Author(s) -
Rodrigues George,
Louie Alexander,
Videtic Gregory,
Best Lara,
Patil Nikhilesh,
Hallock Abhirami,
Gaede Stewart,
Kempe Jeff,
Battista Jerry,
Haan Patricia,
Bauman Glenn
Publication year - 2012
Publication title -
journal of medical imaging and radiation oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.31
H-Index - 43
eISSN - 1754-9485
pISSN - 1754-9477
DOI - 10.1111/j.1754-9485.2012.02442.x
Subject(s) - contouring , medicine , quality assurance , segmentation , metric (unit) , outlier , artificial intelligence , computer science , medical physics , algorithm , pathology , operations management , external quality assessment , computer graphics (images) , economics
Obtaining high levels of contouring consistency is a major limiting step in optimizing the radiotherapeutic ratio. We describe a novel quantitative methodology for the quality assurance ( QA ) of contour compliance referenced against a community set of contouring experts. Methods Two clinical tumour site scenarios (10 lung cases and one prostate case) were used with QA algorithm. For each case, multiple physicians (lung: n = 6, prostate: n = 25) segmented various target/organ at risk ( OAR ) structures to define a set of community reference contours. For each set of community contours, a consensus contour ( S imultaneous T ruth and P erformance L evel E stimation ( STAPLE )) was created. Differences between each individual community contour versus the group consensus contour were quantified by consensus‐based contouring penalty metric ( PM ) scores. New observers segmented these same cases to calculate individual PM scores (for each unique target/ OAR ) for each new observer– STAPLE pair for comparison against the community and consensus contours. Results Four physicians contoured the 10 lung cases for a total of 72 contours for quality assurance evaluation against the previously derived community consensus contours. A total of 16 outlier contours were identified by the QA system of which 11 outliers were due to over‐contouring discrepancies, three were due to over‐/under‐contouring discrepancies, and two were due to missing/incorrect nodal contours. In the prostate scenario involving six physicians, the QA system detected a missing penile bulb contour, systematic inner‐bladder contouring, and under‐contouring of the upper/anterior rectum. Conclusion A practical methodology for QA has been demonstrated with future clinical trial credentialing, medical education and auto‐contouring assessment applications.