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Poster — Wed Eve—43: A Maximum Likelihood/ Simulated Annealing‐Based Validation Method for Tumor Segmentation Techniques
Author(s) -
Yu H,
Caldwell C,
Mah K
Publication year - 2009
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3244147
Subject(s) - segmentation , ground truth , artificial intelligence , probabilistic logic , imaging phantom , computer science , image segmentation , simulated annealing , pattern recognition (psychology) , medical imaging , computer vision , mathematics , nuclear medicine , medicine , algorithm
The performance of tumor segmentation methods can only be evaluated by comparison with observers' contours when the true pathologic extent is unknown. All observers' contours contain bias and it is unclear how to best combine segmentations to estimate “truth”. Objective: To develop a method that optimally combines observers' contours to estimate a “true” reference for evaluating tumor segmentation techniques. Methods: A probabilistic “truth” was estimated from multiple observers' segmentations via maximum‐likelihood analysis using the simulated‐annealing (SA) and Expectation‐Maximum optimization algorithms. A qualitative ranking of the performance levels of observers' segmentations, was introduced to steer the method when the relative qualities of input contours are known. The SA‐based method was evaluated first using digital phantoms which were “true” tumors and simulated observers' contours by shifting, shrinking and expanding the “true” tumors. It, secondly, was evaluated using clinical data. The tumor volumes of 12 head and neck cancer (HNC) patients were contoured by three radiation oncologists using CT alone and subsequently, using PET/CT. Results: The SA‐based method exactly determined the “truth” in digital phantom studies. In the clinical study of HNC patients the mean and the range of sensitivity of the SA method were 0.90 and 0.70–0.98, respectively, when compared to a pre‐defined “probabilistic” reference. Conclusions: This work suggests that the SA method can accurately estimate a “truth” to validate image segmentation methods. It also provides a logical means of combining segmentations from different sources, which could potentially improve accuracy of radiation therapy or surgical target localization.