MRNet: Multifaceted Resilient Networks for Medical Image-to-Image Translation

• URL: http://arxiv.org/abs/2412.03039v1
• Date: Wed, 04 Dec 2024 05:23:46 GMT
• Title: MRNet: Multifaceted Resilient Networks for Medical Image-to-Image Translation
• Authors: Hyojeong Lee, Youngwan Jo, Inpyo Hong, Sanghyun Park,
• Abstract summary: We propose a novel architecture developed for medical image-to-image translation that outperforms state-of-the-art methods in MRI-to-CT and MRI-to-MRI conversion. The architecture extracts comprehensive multiscale features from diverse datasets using a powerful SAM image encoder and performs resolution-aware feature fusion.
• Score: 1.9384004397336387
• License:
• Abstract: We propose a Multifaceted Resilient Network(MRNet), a novel architecture developed for medical image-to-image translation that outperforms state-of-the-art methods in MRI-to-CT and MRI-to-MRI conversion. MRNet leverages the Segment Anything Model (SAM) to exploit frequency-based features to build a powerful method for advanced medical image transformation. The architecture extracts comprehensive multiscale features from diverse datasets using a powerful SAM image encoder and performs resolution-aware feature fusion that consistently integrates U-Net encoder outputs with SAM-derived features. This fusion optimizes the traditional U-Net skip connection while leveraging transformer-based contextual analysis. The translation is complemented by an innovative dual-mask configuration incorporating dynamic attention patterns and a specialized loss function designed to address regional mapping mismatches, preserving both the gross anatomy and tissue details. Extensive validation studies have shown that MRNet outperforms state-of-the-art architectures, particularly in maintaining anatomical fidelity and minimizing translation artifacts.

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