A research on the improved rotational robustness for thoracic organ delineation by using joint learning of segmenting spatially-correlated organs: A U-net based comparison.

Purpose: To study the improved rotational robustness by using joint learning of spatially-correlated organ segmentation (SCOS) for thoracic organ delineation. The network structure is not our point.

Methods: The SCOS was implemented in a U-net-like model (abbr. SCOS-net) and evaluated on unseen rotated test sets. Two hundred sixty-seven patients with thoracic tumors (232 without rotation and 35 with rotation) were enrolled. The training and validation images came from 61 randomly chosen unrotated patients. The test data included two sets. One consisted of 3000 slices from the rest 171 unrotated patients. They were rotated by us by -30°∼30°. One was the images from the 35 rotated patients. The lung, heart, and spinal cord were delineated by experienced radiation oncologists and regarded as ground truth. The SCOS-net was compared with its single-task learning counterparts, two published multiple learning task settings, and rotation augmentation. Dice, 3 distance metrics (maximum and 95th percentile of Hausdorff distances and average surface distance (ASD)) and the number of cases where ASD = infinity were adopted. We analyzed the results using visualization techniques.

Results: In terms of no augmentation, the SCOS-net achieves the best lung and spinal cord segmentations and comparable heart delineation. With augmentation, SCOS performs better in some cases.

Conclusion: The proposed SCOS can improve rotational robustness, and is promising in clinical applications for its low network capacity and computational cost.