Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor Segmentation

• URL: http://arxiv.org/abs/2403.09942v1
• Date: Fri, 15 Mar 2024 00:52:17 GMT
• Title: Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor Segmentation
• Authors: Ziya Ata Yazıcı, İlkay Öksüz, Hazım Kemal Ekenel,
• Abstract summary: Glioblastoma is a highly aggressive and malignant brain tumor type that requires early diagnosis and prompt intervention. To address this challenge, Artificial Intelligence (AI)-driven approaches in healthcare have generated interest in efficiently diagnosing and evaluating brain tumors. In our approach, we utilize a multi-scale, attention-guided and hybrid U-Net-shaped model -- GLIMS -- to perform 3D brain tumor segmentation.
• Score: 0.9897828700959131
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Glioblastoma is a highly aggressive and malignant brain tumor type that requires early diagnosis and prompt intervention. Due to its heterogeneity in appearance, developing automated detection approaches is challenging. To address this challenge, Artificial Intelligence (AI)-driven approaches in healthcare have generated interest in efficiently diagnosing and evaluating brain tumors. The Brain Tumor Segmentation Challenge (BraTS) is a platform for developing and assessing automated techniques for tumor analysis using high-quality, clinically acquired MRI data. In our approach, we utilized a multi-scale, attention-guided and hybrid U-Net-shaped model -- GLIMS -- to perform 3D brain tumor segmentation in three regions: Enhancing Tumor (ET), Tumor Core (TC), and Whole Tumor (WT). The multi-scale feature extraction provides better contextual feature aggregation in high resolutions and the Swin Transformer blocks improve the global feature extraction at deeper levels of the model. The segmentation mask generation in the decoder branch is guided by the attention-refined features gathered from the encoder branch to enhance the important attributes. Moreover, hierarchical supervision is used to train the model efficiently. Our model's performance on the validation set resulted in 92.19, 87.75, and 83.18 Dice Scores and 89.09, 84.67, and 82.15 Lesion-wise Dice Scores in WT, TC, and ET, respectively. The code is publicly available at https://github.com/yaziciz/GLIMS.

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