ESTRO 2021 Abstract Book

S774

ESTRO 2021

Finally, the segmentations from G2 and the neural networks' predictions were compared against the ground truth labels from G1. Results The FCN32, U-Net, and SegNet had average segmentation times of 0.77, 0.48, and 0.43 seconds per image, respectively. The average segmentation time per image for G1 and G2 were 10 and 8 seconds, respectively.

A representative example of a U-Net model's segmentation is compared to the ground truth from G1 in Figure 1. The segmentation scores are presented in Figure 2. All the ground truth labels contained tumors, but G2 and the deep learning models did not always find tumors in the images. The scores are based on the agreement of tumor contours with G1’s ground truth and were thus only computed for images in which tumor was found. The automated segmentation algorithms consistently achieved equal or better scores than G2's manual segmentations. G2's low F1/DICE and precision scores indicate poor agreement between the manual contours.

Conclusion