用解剖结构信息提升医学图像超分辨率效果
SeCo-INR: Semantically Conditioned Implicit Neural Representations for Improved Medical Image Super-Resolution
- 根据医学图像局部解剖特征条件化隐式神经表示
- 在多模态数据上超越现有方法的客观指标和视觉质量
- 适合需要高精度医学图像重建的研究者
隐式神经表示（INRs）因其无需大规模训练数据即可学习信号连续表示的能力，近年来推动了深度学习的发展。尽管已有研究将INR应用于医学图像超分辨率，但其对医学图像中局部先验信息的适应性尚未充分探索。医学图像包含丰富的解剖结构划分，可为提升INRs的准确性和鲁棒性提供重要局部先验。本文提出一种新框架——语义条件化隐式神经表示（SeCo-INR），利用医学图像的局部先验信息对INR进行条件化，实现精准建模与插值，从而完成超分辨率。该框架学习医学图像语义分割特征的连续表示，并据此为图像各语义区域生成最优INR。我们在多种医学成像模态上验证了该框架，结果表明其在定量评分和真实感输出方面均优于当前最先进的方法。
原文摘要 · Abstract (English)
Implicit Neural Representations (INRs) have recently advanced the field of deep learning due to their ability to learn continuous representations of signals without the need for large training datasets. Although INR methods have been studied for medical image super-resolution, their adaptability to localized priors in medical images has not been extensively explored. Medical images contain rich anatomical divisions that could provide valuable local prior information to enhance the accuracy and robustness of INRs. In this work, we propose a novel framework, referred to as the Semantically Conditioned INR (SeCo-INR), that conditions an INR using local priors from a medical image, enabling accurate model fitting and interpolation capabilities to achieve super-resolution. Our framework learns a continuous representation of the semantic segmentation features of a medical image and utilizes it to derive the optimal INR for each semantic region of the image. We tested our framework using several medical imaging modalities and achieved higher quantitative scores and more realistic super-resolution outputs compared to state-of-the-art methods.
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