Evaluation of Automatic Atlas-Based Lymph Node Segmentation for Head-and-Neck Cancer

Purpose To evaluate if automatic atlas-based lymph node segmentation (LNS) improves efficiency and decreases inter-observer variability while maintaining accuracy. Methods and Materials Five physicians with head-and-neck IMRT experience used computed tomography (CT) data from 5 patients to create bilateral neck clinical target volumes covering specified nodal levels. A second contour set was automatically generated using a commercially available atlas. Physicians modified the automatic contours to make them acceptable for treatment planning. To assess contour variability, the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm was used to take collections of contours and calculate a probabilistic estimate of the "true" segmentation. Differences between the manual, automatic, and automatic-modified (AM) contours were analyzed using multiple metrics. Results Compared with the "true" segmentation created from manual contours, the automatic contours had a high degree of accuracy, with sensitivity, Dice similarity coefficient, and mean/max surface disagreement values comparable to the average manual contour (86%, 76%, 3.3/17.4 mm automatic vs. 73%, 79%, 2.8/17 mm manual). The AM group was more consistent than the manual group for multiple metrics, most notably reducing the range of contour volume (106–430 mL manual vs. 176–347 mL AM) and percent false positivity (1–37% manual vs. 1–7% AM). Average contouring time savings with the automatic segmentation was 11.5 min per patient, a 35% reduction. Conclusions Using the STAPLE algorithm to generate "true" contours from multiple physician contours, we demonstrated that, in comparison with manual segmentation, atlas-based automatic LNS for head-and-neck cancer is accurate, efficient, and reduces interobserver variability. To evaluate if automatic atlas-based lymph node segmentation (LNS) improves efficiency and decreases inter-observer variability while maintaining accuracy. Five physicians with head-and-neck IMRT experience used computed tomography (CT) data from 5 patients to create bilateral neck clinical target volumes covering specified nodal levels. A second contour set was automatically generated using a commercially available atlas. Physicians modified the automatic contours to make them acceptable for treatment planning. To assess contour variability, the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm was used to take collections of contours and calculate a probabilistic estimate of the "true" segmentation. Differences between the manual, automatic, and automatic-modified (AM) contours were analyzed using multiple metrics. Compared with the "true" segmentation created from manual contours, the automatic contours had a high degree of accuracy, with sensitivity, Dice similarity coefficient, and mean/max surface disagreement values comparable to the average manual contour (86%, 76%, 3.3/17.4 mm automatic vs. 73%, 79%, 2.8/17 mm manual). The AM group was more consistent than the manual group for multiple metrics, most notably reducing the range of contour volume (106–430 mL manual vs. 176–347 mL AM) and percent false positivity (1–37% manual vs. 1–7% AM). Average contouring time savings with the automatic segmentation was 11.5 min per patient, a 35% reduction. Using the STAPLE algorithm to generate "true" contours from multiple physician contours, we demonstrated that, in comparison with manual segmentation, atlas-based automatic LNS for head-and-neck cancer is accurate, efficient, and reduces interobserver variability.