ADB-Net: an attention-based dilated bridge model for fully automatic intra-tumour segmentation of gliomas
by Radhika Malhotra; Barjinder Singh Saini; Savita Gupta
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 44, No. 3, 2024

Abstract: Glioma segmentation is a complicated task due to the non-uniform and unstructured morphology of gliomas. Moreover, the requirement for trainable parameters grows exponentially with architectural advancements. In this work, a lightweight and modified attention-based dilated bridge net (ADB-Net) architecture is developed for accurate segmentation of glioma sub-regions. The proposed work has four main benefactions. Firstly, the bridging network in D-Link is enhanced by incorporating a deformed residual connection after each dilation convolutional block to promote the mapping of multi-level information between encoding/decoding units. Secondly, a proper selection of the dilation factor is included for dilated convolutional blocks. Thirdly, four modified attention skip modules (ASM) are also introduced to provide recognition of varied-sized tumours. Lastly, the proposed architecture outperforms its baselines while minimising the number of trainable parameters by more than 50%. It achieves dice scores for the complete tumour, tumour core, and enhancing tumour as 0.971, 0.979, and 0.962, respectively.

Online publication date: Wed, 13-Mar-2024

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