Empirical combination networks for head and neck organs at risk segmentation

Le résumé fourni par la source

To comprehensively evaluate popular medical segmentation networks on the CSTRO dataset for head and neck organs at risk (H&N OARs) segmentation, identify top performers, and integrate them into a robust hybrid network (Attention W-Net) for superior performance. U-Net, Attention U-Net, R2U-Net, UNet-plusplus, and CE-Net were selected and two novel architectures W-Net and SE-U-Net were developed. Using U-Net as the baseline, a first-stage experiment was conducted to evaluate the segmentation performance of these networks. Following initial evaluations, Attention U-Net, SE-U-Net, and W-Net achieved notably strong performance. Representative blocks were identified and extracted from these three networks to construct three hybrid architectures: Attention W-Net, SEW-Net, and Attention SEU-Net. Subsequently, a second-stage experiment was conducted to determine the optimal hybrid architecture. In the first stage, U-Net, Attention U-Net, R2U-Net, UNet-plusplus, CE-Net, W-Net and SEU-Net were tested and achieved 0.712, 0.755, 0.706, 0.710, 0.702, 0.708, 0.767, 0.749 of average dice similarity coefficient (DSC), respectively. Then the best three networks Attention U-Net, SEU-Net, W-Net were selected out. The hybrid networks Attention W-Net, Attention SEU-Net, SEW-Net were tested, and achieved 0.776, 0.768, 0.743 of average (DSC), respectively. In terms of the metric, Attention W-Net is the most effective networks for H&N OAR segmentation. The Attention W-Net and SEW-Net are the better networks which achieve better results than the popular state-of-the-arts networks for head and neck OARs segmentation.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.