A lightweight multiscale attention network for 3D tumor segmentation in PET images.

Accurate segmentation of tumor PET images has a significance in disease monitoring and clinical treatment. However, the task of accurately segmenting PET images remains challenging due to insufficient access to spatial features of PET images. Therefore, we propose a multiscale attention-guided feature mechanism for 3D tumor segmentation (MA3DSeg) to achieve accurate segmentation of tumor PET images. Specifically, we first utilize a 3D-DX block to construct a lightweight encoder to minimise the redundant information generated across channel features and reduce the model complexity. Additionally, we employ multiscale attention-guided feature enhancement (MAFE) module for 3D networks to fuse channel features and spatial features to fully acquire spatial feature analysis of tumors. Furthermore, we propose explicit and implicit difference information in interaction boundary semantic (IBS) module to enhance the representation of segmentation edges. We conduct rigorous experiments on three publicly available datasets ECPC-IDS, Hecktor 2022 and AutoPET, which demonstrated superior performance than other competitive networks. MA3DSeg significantly improves Dice by 0.5% on Hecktor 2022 and RVD by 2.1% on ECPC-IDS compared with 3D UX-Net. The experimental results show that MA3DSeg achieves excellent tumor segmentation performance and generalization ability.