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

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Název: KIDBA‐Net: A Multi‐Feature Fusion Brain Tumor Segmentation Network Utilizing Kernel Inception Depthwise Convolution and Bi‐Cross Attention
Autoři: Jie Min, Tongyuan Huang, Boxiong Huang, Chuanxin Hu, Zhixing Zhang
Zdroj: International Journal of Imaging Systems and Technology. 35
Informace o vydavateli: Wiley, 2025.
Rok vydání: 2025
Popis: 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.
Druh dokumentu: Article
Jazyk: English
ISSN: 1098-1098
0899-9457
DOI: 10.1002/ima.70055
Rights: Wiley Online Library User Agreement
Přístupové číslo: edsair.doi...........73fcd7b7192e62ba4c8f33ea486a6c30
Databáze: OpenAIRE
Popis
Abstrakt: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.
ISSN:10981098
08999457
DOI:10.1002/ima.70055