arXiv:2412.10452eess.IVcs.AI2024-12

用冷冻切片数据给MRI图像上色,保持解剖结构一致

Structurally Consistent MRI Colorization using Cross-modal Fusion Learning

  • 融合冷冻切片的分割语义信息实现稳定上色
  • 无需精确配准或MRI分割,颜色转移更鲁棒
  • 通过特征压缩提升器官级全局信息,上色更真实

医学图像着色可显著提升成像模态的可解释性,并揭示人体解剖结构。本文旨在将冷冻切片数据中分布于人体各部位的丰富色彩,准确迁移至源MRI图像,同时保留其解剖结构。为此,提出一种新型结构一致的色彩迁移架构,通过融合冷冻切片图像的分割语义信息,实现对MRI图像中不同器官的稳定上下文着色。该方法无需精确配准MRI与冷冻切片图像,也无需对MRI进行分割。此外,架构引入特征压缩与激活机制,以捕获器官级别的全局信息并抑制噪声,从而更好地区分MRI扫描中的器官特异性数据,实现更准确、更逼真的器官特异性着色。实验表明,本方法在定量和定性指标上均优于现有方法。

原文摘要 · Abstract (English)

Medical image colorization can greatly enhance the interpretability of the underlying imaging modality and provide insights into human anatomy. The objective of medical image colorization is to transfer a diverse spectrum of colors distributed across human anatomy from Cryosection data to source MRI data while retaining the structures of the MRI. To achieve this, we propose a novel architecture for structurally consistent color transfer to the source MRI data. Our architecture fuses segmentation semantics of Cryosection images for stable contextual colorization of various organs in MRI images. For colorization, we neither require precise registration between MRI and Cryosection images, nor segmentation of MRI images. Additionally, our architecture incorporates a feature compression-and-activation mechanism to capture organ-level global information and suppress noise, enabling the distinction of organ-specific data in MRI scans for more accurate and realistic organ-specific colorization. Our experiments demonstrate that our architecture surpasses the existing methods and yields better quantitative and qualitative results.

医学图像图像着色跨模态融合MRI

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