A Transfer Learning Approach for Improving OAR Segmentation in the Adaptive Therapy or Retreatment of Head and Neck Cancer

Purpose/objective(s) Deep learning-based segmentation has achieved great progress compared with traditional method. However, the generalizability is still an unsolved problem, leading to suboptimal contours for some cases that requires manual editing. A big source of error lies in the abnormal patient anatomy that was not well covered by the training data. In adaptive or retreatment settings, a well-contoured CT is available. However, the current DLSEG algorithm does not allow such information to be utilized. Our proposed solution is to use transfer learning method to tune a trained DLSEG model toward individual patient using the prior segmented scans. Materials/methods A HN DLSEG model was trained on 2015 MICCAI Head and Neck dataset. The model was applied on HNSCC-3DCT-RT dataset from TCIA which contains 3 CT scans at pre-, mid-, and end- treatment course. The DLSEG result was compared with the ground truth contour in the study. Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD) were computed for BrainStem, Mandible, Parotid, SpinalCord and OpticNerve. The standard deviations of these metrics were computed, both within the same patient (IntraPatient variation) and across patients (InterPatient variation). Then, the DLSEG was re-tuned using transfer learning on the pre- treatment CT scan of each individual patient. The re-tuned model was applied to the mid- and end- treatment CT. The segmentation accuracy was evaluated. Results All structures showed a much larger InterPatient variation than IntraPatient variation, indicating that patient specific anatomical feature is one of the main contributors to segmentation accuracy. When transfer learning is applied. Nearly all patients saw improvement in contouring accuracy. Statistically significant improvement was observed for BrainStem, Mandible, Parotid and Parotid_L. Conclusion Transfer learning is a promising approach to incorporate the existing segmentation on prior CT to improve the segmentation accuracy in the adaptive or re-treatment settings.