Artifact-driven sampling schemes for robust female pelvis CBCT segmentation using deep learning.

Adaptive radiotherapy (RT) planning requires segmentation of organs for adapting the RT treatment plan to changes in the patient's anatomy. Daily imaging is often done using cone-beam CT (CBCT) imaging devices which produce images of considerably lower quality than CT images, due to scatter and artifacts. Involuntary patient motion during the comparably long CBCT image acquisition may cause misalignment artifacts. In the pelvis, most severe artifacts stem from motion of air and soft tissue boundaries in the bowel, which appear as streaking in the reconstructed images. In addition to low soft tissue contrast, this makes segmentation of organs close to the bowel such as bladder and uterus even more difficult. Deep learning (DL) methods have shown to be promising for difficult segmentation tasks. In this work, we investigate different, artifact-driven sampling schemes that incorporate domain knowledge into the DL training. However, global evaluation metrics such as the Dice score, often used in DL segmentation research, reveal little information about systematic errors and no clear perspective how to improve the training. Using slice-wise Dice scores, we find a clear difference in performance on slices with and without air detected. Moreover, especially when applied in a curriculum training scheme, the specific sampling of slices on which air has been detected might help to increase robustness of deep neural networks towards artifacts while maintaining performance on artifact-free slices.