Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings

2025-02-04

AI Summary

Automatic brain tumor segmentation in resource-constrained regions—such as sub-Saharan Africa—faces critical challenges including limited computational infrastructure, scarcity of expert annotations, and poor generalizability of deep learning models. Method: We propose the first 3D multi-architecture deep ensemble method tailored for low-resource medical settings, integrating UNet3D, V-Net, and MSA-VNet. Our approach employs cross-dataset transfer learning—pretraining on BraTS-GLI and fine-tuning on BraTS-SSA—and incorporates multi-task semantic segmentation optimization to enhance feature representation and regularization. Contribution/Results: The ensemble significantly improves model robustness and clinical deployability, achieving DICE scores of 0.8358 (tumor core), 0.8521 (whole tumor), and 0.8167 (enhancing tumor)—consistently outperforming individual architectures. This validates the efficacy and practical feasibility of deep ensembling under data heterogeneity and severe resource constraints.

Technology Category

• Machine Learning: Ensemble Methods
• Computer Vision: Segmentation
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Segmentation of brain tumors is a critical step in treatment planning, yet manual segmentation is both time-consuming and subjective, relying heavily on the expertise of radiologists. In Sub-Saharan Africa, this challenge is magnified by overburdened medical systems and limited access to advanced imaging modalities and expert radiologists. Automating brain tumor segmentation using deep learning offers a promising solution. Convolutional Neural Networks (CNNs), especially the U-Net architecture, have shown significant potential. However, a major challenge remains: achieving generalizability across different datasets. This study addresses this gap by developing a deep learning ensemble that integrates UNet3D, V-Net, and MSA-VNet models for the semantic segmentation of gliomas. By initially training on the BraTS-GLI dataset and fine-tuning with the BraTS-SSA dataset, we enhance model performance. Our ensemble approach significantly outperforms individual models, achieving DICE scores of 0.8358 for Tumor Core, 0.8521 for Whole Tumor, and 0.8167 for Enhancing Tumor. These results underscore the potential of ensemble methods in improving the accuracy and reliability of automated brain tumor segmentation, particularly in resource-limited settings.

Problem

• Improving brain tumor segmentation accuracy
• Addressing generalizability across datasets
• Enhancing segmentation in resource-limited settings

Innovation

• Deep Ensemble for segmentation
• Integrates UNet3D, V-Net, MSA-VNet
• Fine-tuning with BraTS datasets