I2-Net: Intra- and Inter-scale Collaborative Learning Network for Abdominal Multi-organ Segmentation

Efficient and accurate abdominal multi-organ segmentation is the key to clinical applications such as computer-aided diagnosis and computer-aided surgery, but this task is extremely challenging due to blurred organ boundaries, complex backgrounds, and different organ sizes. Although existing segmentation methods have achieved good segmentation results, we found that the segmentation performance of abdominal small and medium organs is often unsatisfactory, but the accurate location and segmentation of abdominal small and medium organs plays an important role in the diagnosis and screening of clinical diseases. To address this problem, in this paper we propose an intra- and inter-scale collaborative learning network (I2-Net) for the abdominal multi-organ segmentation task. Firstly, we design a Feature Complementary Module (FCM) to adaptively complement the local and global features extracted by CNN and Transformer. Secondly, we propose a Feature Aggregation Module (FAM) to aggregate multi-scale semantic information. Finally, we employ a Focus Module (FM) for collaborative learning of intra- and inter-scale features. Extensive experiments on the Synapse dataset show that our method outperforms the state-of-the-art approaches and achieve accurate segmentation of abdominal multi-organs, especially for small and medium organs.