Enhancement and Segmentation of High Definition CT Images in Everything 6G Medical IoT Environment

Integrating medical Internet of Things (IoT) systems with 6G networks presents unprecedented real-time medical image analysis opportunities. However, it introduces significant challenges when models trained on one device fail to generalize to images from different equipment. This paper proposes enhanced target domain representation for cross-domain medical image segmentation (E-TDRCMIS), a novel high-definition CT image segmentation approach in 6G medical IoT environments. Our method addresses domain shift challenges through a three-component architecture optimized for distributed healthcare systems. Our shared feature learning module employs adversarial techniques with gradient reversal layers to extract domain-invariant features while minimizing computational overhead at network edges. The shared feature enhancement module implements a multi-level consistency regularization strategy that compares predictions from shallow and deep features to strengthen feature representation without increasing bandwidth requirements. Finally, the target domain generalization module reconstructs complete feature representations. Experiments on chest and spine structure datasets demonstrate that E-TDRCMIS significantly outperforms existing methods in cross-domain high-definition CT image segmentation. Specifically, E-TDRCMIS achieved Dice scores of 87.9% and 91.3% on the LUNA16 and VerSe2020 datasets, respectively, with corresponding average symmetric surface distance (ASD) values of 1.2mm and 0.9mm. This represents improvements of 3.0% (Dice) and 20.0% (ASD) over the next best performing method (MRLA-Net) on LUNA16, and 2.5% (Dice) and 30.8% (ASD) improvements on VerSe2020.