KIDBA-Net: A Multi-Feature Fusion Brain Tumor Segmentation Network Utilizing Kernel Inception Depthwise Convolution and Bi-Cross Attention

Abstract

Automatic brain tumor segmentation technology plays a crucial role in tumor diagnosis, particularly in the precise delineation of tumor subregions. It can assist doctors in accurately assessing the type and location of brain tumors, potentially saving patients' lives. However, the highly variable size and shape of brain tumors, along with their similarity to healthy tissue, pose significant challenges in the segmentation of multi-label brain tumor subregions. This paper proposes a network model, KIDBA-Net, based on an encoder-decoder architecture, aimed at solving the issue of pixel-level classification errors in multi-label tumor subregions. The proposed Kernel Inception Depthwise Block (KIDB) employs multi-kernel depthwise convolution to extract multi-scale features in parallel, accurately capturing the feature differences between tumor types to mitigate misclassification. To ensure the network focuses more on the lesion areas and excludes the interference of irrelevant tissues, this paper adopts Bi-Cross Attention as a skip connection hub to bridge the semantic gap between layers. Additionally, the Dynamic Feature Reconstruction Block (DFRB) exploits the complementary advantages of convolution and dynamic upsampling operators, effectively aiding the model in generating high-resolution prediction maps during the decoding phase. The proposed model surpasses other state-of-the-art brain tumor segmentation methods on the BraTS2018 and BraTS2019 datasets, particularly in the segmentation accuracy of smaller and highly overlapping tumor core (TC) and enhanced tumor (ET), achieving DSC scores of 87.8%, 82.0%, and 90.2%, 88.7%, respectively; Hausdorff distances of 2.8, 2.7 mm, and 2.7, 2.0 mm.