|Author||Lamichhane, N. ♦ Johnson, P. ♦ Chinea, F. ♦ Patel, V. ♦ Yang, F.|
|Source||United States Department of Energy Office of Scientific and Technical Information|
|Subject Keyword||APPLIED LIFE SCIENCES ♦ RADIATION PROTECTION AND DOSIMETRY ♦ ACCURACY ♦ BIOMEDICAL RADIOGRAPHY ♦ COMPUTERIZED SIMULATION ♦ IMAGES ♦ MEDICAL PERSONNEL ♦ MONTE CARLO METHOD ♦ NEOPLASMS ♦ PHANTOMS ♦ POSITRON COMPUTED TOMOGRAPHY ♦ STATISTICAL MODELS|
|Abstract||Purpose: To evaluate the correlation between image features and the accuracy of manually drawn target contours on synthetic PET images Methods: A digital PET phantom was used in combination with Monte Carlo simulation to create a set of 26 simulated PET images featuring a variety of tumor shapes and activity heterogeneity. These tumor volumes were used as a gold standard in comparisons with manual contours delineated by 10 radiation oncologist on the simulated PET images. Metrics used to evaluate segmentation accuracy included the dice coefficient, false positive dice, false negative dice, symmetric mean absolute surface distance, and absolute volumetric difference. Image features extracted from the simulated tumors consisted of volume, shape complexity, mean curvature, and intensity contrast along with five texture features derived from the gray-level neighborhood difference matrices including contrast, coarseness, busyness, strength, and complexity. Correlation between these features and contouring accuracy were examined. Results: Contour accuracy was reasonably well correlated with a variety of image features. Dice coefficient ranged from 0.7 to 0.90 and was correlated closely with contrast (r=0.43, p=0.02) and complexity (r=0.5, p<0.001). False negative dice ranged from 0.10 to 0.50 and was correlated closely with contrast (r=0.68, p<0.001) and complexity (r=0.66, p<0.001). Absolute volumetric difference ranged from 0.0002 to 0.67 and was correlated closely with coarseness (r=0.46, p=0.02) and complexity (r=0.49, p=0.008). Symmetric mean absolute difference ranged from 0.02 to 1 and was correlated closely with mean curvature (r=0.57, p=0.02) and contrast (r=0.6, p=0.001). Conclusion: The long term goal of this study is to assess whether contouring variability can be reduced by providing feedback to the practitioner based on image feature analysis. The results are encouraging and will be used to develop a statistical model which will enable a prediction of contour accuracy based purely on image feature analysis.|
|Learning Resource Type||Article|
|Publisher Place||United States|
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