A 7T MRI-Guided Learning Method for Automatic Hippocampal Subfield Segmentation on Routine 3T MRI

Abstract

Accurate segmentation of hippocampal subfields in MRI scans is crucial for aiding in the diagnosis of various neurological diseases and for monitoring brain states. However, due to limitations of imaging systems and the inherent complexity of hippocampal subfield delineation, achieving accurate hippocampal subfield delineation on routine 3T MRI is highly challenging. In this paper, we propose a novel Guided Learning Network (GLNet) that leverages 7T MRI to enhance the accuracy of hippocampal subfield segmentation on routine 3T MRI. GLNet aligns and learns shared features between 3T MRI and 7T MRI through a modeling approach based on domain-specific and domain-shared feature learning, leveraging the features of 7T MRI to guide learning for 3T MRI features. In this process, we also introduce a Multi-Feature Attention Fusion (MFAF) block to integrate both specific and shared features from each modality. By leveraging an attention mechanism, MFAF adaptively focuses on relevant information between the specific and shared features within the same modality, thereby reducing the impact of irrelevant information. Additionally, we further proposed an Online Knowledge Distillation (OLKD) method to distill detailed knowledge from 7T MRI into 3T MRI, enhancing the feature representation capability and robustness of the 3T MRI segmentation model. Our method was validated on PAIRED 3T-7T HIPPOCAMPAL SUBFIELD DATASET, and the experimental results demonstrate that GLNet outperforms other competitive methods.