An improved 3D KiU-Net for segmentation of liver tumor.

It is a challenging task to accurately segment liver tumors from Computed Tomography (CT) images. The widely used U-Net and its variants generally suffer from the issue to accurately segment the detailed edges of small tumors, because the progressive down sampling operations in the encoder module will gradually increase the receptive fields. These enlarged receptive filed have limited ability to learn the information about tiny structures. KiU-Net is a newly proposed dual-branch model that can effectively perform image segmentation for small targets. However, the 3D version of KiU-Net has high computational complexity, which limits its application. In this work, an improved 3D KiU-Net (named TKiU-NeXt) is proposed for liver tumor segmentation from CT images. In TKiU-NeXt, a Transformer-based Kite-Net (TK-Net) branch is proposed to build the over-complete architecture to learn more detailed features for small structures, and an extended 3D version of UNeXt is developed to replace the original U-Net branch, which can effectively reduce computational complexity but still with superior segmentation performance. Moreover, a Mutual Guided Fusion Block (MGFB) is designed to effectively learn more features from two branches and then fuse the complementary features for image segmentation. The experimental results on two public CT datasets and a private dataset demonstrate that the proposed TKiU-NeXt outperforms all the compared algorithms, and it also has less computational complexity. It suggests the effectiveness and efficiency of TKiU-NeXt.