Attention-gated U-Net model for semantic segmentation of brain tumors and feature extraction for survival prognosis

2026-02-14

AI Summary

Gliomas exhibit high heterogeneity in invasiveness, prognosis, and histology, necessitating precise segmentation and survival prediction methods. This work proposes an attention-gated recurrent residual U-Net with triplanar (2.5D) inputs—termed Attention-Gated R2U-Net—that uniquely integrates attention gates, residual connections, recurrent structures, and multiplanar input to enhance both segmentation accuracy and survival prediction while maintaining computational efficiency. Evaluated on the BraTS2021 validation set, the model achieves a Dice score of 0.900 for whole-tumor segmentation. Leveraging the extracted multiplanar features, an artificial neural network (ANN) is employed for survival regression, yielding a prediction accuracy of 45.71%, a mean squared error (MSE) of 108,318.128, and a Spearman correlation coefficient of 0.338.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Multimodal Learning
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Gliomas, among the most common primary brain tumors, vary widely in aggressiveness, prognosis, and histology, making treatment challenging due to complex and time-intensive surgical interventions. This study presents an Attention-Gated Recurrent Residual U-Net (R2U-Net) based Triplanar (2.5D) model for improved brain tumor segmentation. The proposed model enhances feature representation and segmentation accuracy by integrating residual, recurrent, and triplanar architectures while maintaining computational efficiency, potentially aiding in better treatment planning. The proposed method achieves a Dice Similarity Score (DSC) of 0.900 for Whole Tumor (WT) segmentation on the BraTS2021 validation set, demonstrating performance comparable to leading models. Additionally, the triplanar network extracts 64 features per planar model for survival days prediction, which are reduced to 28 using an Artificial Neural Network (ANN). This approach achieves an accuracy of 45.71%, a Mean Squared Error (MSE) of 108,318.128, and a Spearman Rank Correlation Coefficient (SRC) of 0.338 on the test dataset.

Problem

• brain tumor segmentation
• survival prognosis
• glioma
• semantic segmentation
• feature extraction

Innovation

• Attention-Gated R2U-Net
• Triplanar 2.5D Segmentation
• Brain Tumor Semantic Segmentation
• Survival Prognosis Feature Extraction
• Residual Recurrent Architecture