Comparative evaluation of a prototype deep learning algorithm for autosegmentation of normal tissues in head and neck radiotherapy. (September 2022) - Koo, Jihye; Caudell, Jimmy J.; Latifi, Kujtim; Jordan, Petr; Shen, Sangyu; Adamson, Philip M.; Moros, Eduardo G.; Feygelman, Vladimir
Highlights: New Deep Learning-based auto-segmentation algorithm for H&N OARs described. Produced higher comparison scores compared to previously published. 93% of the auto-segmented structures were subjectively judged as clinically useful. The training dataset must be congruent with the expectations of the evaluator. Performance can differ when the same algorithm is trained at different institutions. Abstract: Purpose: To introduce and validate a newly developed deep-learning (DL) auto-segmentation algorithm for head and neck (HN) organs at risk (OARs) and to compare its performance with a published commercial algorithm. Methods: A total of 864 HN cancer cases were available to train and evaluate a prototype algorithm. The algorithm is based on a fully convolutional network with combined U-Net and V-net. A Dice loss plus Cross-Entropy Loss function with Adam optimizer was used in training. For 75 validation cases, OAR sets were generated with three DL-based models (A: the prototype model trained with gold data, B: a commercial software trained with the same data, and C: the same software trained with data from another institution). The auto-segmented structures were evaluated with Dice similarity coefficient (DSC), Hausdorff distance (HD), voxel-penalty metric (VPM) and DSC of area under dose-volume histograms. A subjective qualitative evaluation was performed on 20 random cases. Results: Overall trend was for the prototype algorithm to be the closest to the gold data by all five metrics. The average DSC/VPM/HD for algorithms A, B, and C were 0.81/84.1/1.6 mm, 0.74/62.8/3.2 mm, and 0.66/46.8/3.3 mm, respectively. 93% of model A structures were evaluated to be clinically useful. Conclusion: The superior performance of the prototype was validated, even when trained with the same data. In addition to the challenges of perfecting the algorithms, the auto-segmentation results can differ when the same algorithm is trained at different institutions.