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• W4220772041 abstract "<h3>Purpose/Objective(s)</h3> Segmentation of head and neck (HN) organs at risk (OARs) is a laborious process. Here we introduce and validate a newly developed deep-learning-based auto-segmentation program and compare with a commercially available system, and the same system trained on internal data. <h3>Materials/Methods</h3> A total of 864 previously treated HN cancer patients were available to train and evaluate a prototype deep learning-based normal tissue 3D auto-segmentation algorithm. The algorithm is based on a fully convolutional network with U-Net and V-net features combined as the backbone of the network. A Dice loss function with the Adam optimizer was used in training the models with between 150-500 patients used per model. The OARs were delineated by a single experienced physician (gold data). A subset of 75 cases was withheld from training and used for validation. On those, we generated new OAR sets with three different deep-learning models and compared to the gold data: A) the prototype model trained with gold data, B) a commercial software package trained with the gold data (n=213), and C) the same commercial software with the model trained at another institution (n=589). The agreement between the gold data and auto-segmented structures was evaluated with Dice similarity coefficient (DSC) and voxel-penalty metric that penalizes each missing or extra pixel as a function of distance with forgiveness threshold distance. An ANOVA test with post hoc pair-wise analysis was performed to assess the differences in those metrics. The auto-segmented contours were also qualitatively evaluated by the physician on a scale of 0-5. <h3>Results</h3> The average DSC and voxel penalty metric scores for algorithms A, B, and C across all OARs in the 75 evaluation cases were 0.80/77.68, 0.74/62.75, and 0.66/45.26, respectively. The difference in mean DSC scores was statistically significant (p<0.05) for all 11 OARs where the data for all three algorithms were available. The A/B difference was significant in 6 OARs. Algorithm A scored the highest DSC and voxel penalty metric score in all OARs except for the pharyngeal constrictors. All OARs except for the pharyngeal constrictors showed DSC≥0.7 with algorithm A. For three structures the mean DSC was significantly different between the same algorithm trained at different institutions(B/C). From the qualitative evaluation by a blinded expert, 51 structures (20.2%) of model A were clinically acceptable without edits. The percentages of ‘clinically useful' scores were largest in model A (95.2%) followed by model B (88.0%) and C (80.6%). <h3>Conclusion</h3> The prototype algorithm had improved performance compared to a commercial algorithm, even when trained on data from the same institution. Auto segmentation results can differ significantly when the same algorithm is trained on data from different institutions." @default.
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• W4220772041 date "2022-04-01" @default.
• W4220772041 modified "2023-10-06" @default.
• W4220772041 title "Development of a Deep Learning-Based Auto-Segmentation of Organs at Risk for Head and Neck Radiotherapy Planning" @default.
• W4220772041 doi "https://doi.org/10.1016/j.ijrobp.2021.12.024" @default.
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