LFC-SATP-SGG: Latent Feature Completion and State-Aware Text Prompting with Semantic-Guided Gating for Incomplete...

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Neuroscience

Background:

• Multi-modal Magnetic Resonance Imaging (MRI) is vital for accurate brain tumor segmentation.
• Missing modalities in clinical MRI scans significantly impair deep learning model performance.
• Current methods for handling missing data, such as synthesis or zero-filling, introduce artifacts or lose crucial information.

Purpose of the Study:

• To develop a robust framework for brain tumor segmentation that addresses the challenge of missing MRI modalities.
• To improve the performance of deep learning models in scenarios with incomplete multi-modal MRI data.
• To introduce a unified approach that integrates feature completion, state-aware prompting, and semantic guidance.

Main Methods:

• Latent Feature Completion (LFC): Dynamically generates substitute vectors in the latent space to preserve feature integrity without high computational costs.
• State-Aware Text Prompting (SATP): Automatically encodes modality availability into semantic text prompts, guiding cross-modal attention via a pre-trained text encoder.
• Semantic-Guided Gating (SGG): Refines texture representations using deep semantic features to reduce noise.

Main Results:

• The proposed LFC-SATP-SGG framework demonstrates competitive performance across various missing modality scenarios.
• Experiments on BraTS 2018, 2020, and 2021 datasets validate the method's effectiveness.
• The approach successfully maintains feature distribution integrity and enhances segmentation accuracy.

Conclusions:

• LFC-SATP-SGG offers an effective solution for multi-modal MRI brain tumor segmentation with missing data.
• The framework provides a unified and efficient approach, outperforming existing methods.
• The developed method enhances segmentation precision in challenging clinical settings.