SeCo-INR：面向改进医学图像超分辨率的语义条件隐式神经表示
SeCo-INR: Semantically Conditioned Implicit Neural Representations for Improved Medical Image Super-Resolution
AI总结：
本文提出SeCo-INR框架，利用医学图像语义分割提供的局部先验条件化隐式神经表示，为各语义区域推导最优INR，在多种医学成像模态上实现了优于现有方法的超分辨率效果。
摘要：
隐式神经表示（INRs）由于能够在无需大型训练数据集的情况下学习信号的连续表示，近年来推动了深度学习领域的发展。尽管INR方法已被研究用于医学图像超分辨率，但它们在医学图像中对局部先验的适应性尚未得到广泛探索。医学图像包含丰富的解剖分区，这些分区可以提供有价值的局部先验信息，以提高INR的准确性和鲁棒性。在这项工作中，我们提出了一种新颖的框架，称为语义条件INR（SeCo-INR），该框架利用来自医学图像的局部先验对INR进行条件化，从而实现精确的模型拟合和插值能力，以达到超分辨率。我们的框架学习医学图像语义分割特征的连续表示，并利用该表示为图像的每个语义区域推导出最优的INR。我们使用多种医学成像模态对框架进行了测试，与最先进的方法相比，取得了更高的定量分数和更逼真的超分辨率输出。
英文摘要：
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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