A convolutional neural network algorithm for automatic segmentation of head and neck organs at risk using deep lifelong learning

Abstract

Purpose This study suggests a lifelong learning‐based convolutional neural network ( LL ‐ CNN ) algorithm as a superior alternative to single‐task learning approaches for automatic segmentation of head and neck ( OAR s) organs at risk. Methods and materials Lifelong learning‐based convolutional neural network was trained on twelve head and neck OAR s simultaneously using a multitask learning framework. Once the weights of the shared network were established, the final multitask convolutional layer was replaced by a single‐task convolutional layer. The single‐task transfer learning network was trained on each OAR separately with early stoppage. The accuracy of LL ‐ CNN was assessed based on Dice score and root‐mean‐square error ( RMSE ) compared to manually delineated contours set as the gold standard. LL ‐ CNN was compared with 2D‐ UN et, 3D‐ UN et, a single‐task CNN ( ST ‐ CNN ), and a pure multitask CNN ( MT ‐ CNN ). Training, validation, and testing followed Kaggle competition rules, where 160 patients were used for training, 20 were used for internal validation, and 20 in a separate test set were used to report final prediction accuracies. Results On average contours generated with LL ‐ CNN had higher Dice coefficients and lower RMSE than 2D‐ UN et, 3D‐Unet, ST ‐ CNN , and MT ‐ CNN . LL ‐ CNN required ~72 hrs to train using a distributed learning framework on 2 Nvidia 1080Ti graphics processing units. LL ‐ CNN required 20 s to predict all 12 OAR s, which was approximately as fast as the fastest alternative methods with the exception of MT ‐ CNN . Conclusions This study demonstrated that for head and neck organs at risk, LL ‐ CNN achieves a prediction accuracy superior to all alternative algorithms.