利用影像组学特征分析实现自动化MRI关键点检测：增强图应用
Utilizing Radiomic Feature Analysis For Automated MRI Keypoint Detection: Enhancing Graph Applications
AI总结：
针对脑MRI图像中SIFT和LoFTR难以检测一致初始关键点的问题，提出基于影像组学特征的关键点检测方法，并将其作为LK-SuperRetina的真值，进而展示GNN在图像匹配中的优越性能，为扩展GNN应用奠定基础。
摘要：
图神经网络（GNNs）在特定图像处理应用中，因其在建模空间关系方面的参数效率，成为卷积神经网络（CNNs）和Transformer的一种有前景的替代方案。当前，一个主要研究方向是将非图输入数据转换为适用于基于GNN模型的形式，尤其是在数据来源于图像的场景中。一种方法是通过识别图像中的重要关键点将图像转换为节点。Super-Retina是一种半监督技术，已被用于检测视网膜图像中的关键点。然而，其局限性在于依赖一个较小的初始真值关键点集合，该集合随后被逐步扩展以检测更多关键点。由于在使用SIFT和LoFTR检测脑图像中一致的初始关键点时遇到困难，我们提出了一种新方法：基于影像组学特征的关键点检测。通过展示所检测关键点在改善由其引导的配准过程中的有效性，证明了这些关键点的解剖学意义。随后，这些关键点被用作关键点检测方法（LK-SuperRetina）的真值。此外，本研究展示了GNNs在图像匹配中的应用，突显了其在良好匹配数量和置信度分数方面的优越性能。该研究为将GNN应用扩展到其他多种应用奠定了基础，包括但不限于图像分类、分割和配准。
英文摘要：
Graph neural networks (GNNs) present a promising alternative to CNNs and transformers in certain image processing applications due to their parameter-efficiency in modeling spatial relationships. Currently, a major area of research involves the converting non-graph input data for GNN-based models, notably in scenarios where the data originates from images. One approach involves converting images into nodes by identifying significant keypoints within them. Super-Retina, a semi-supervised technique, has been utilized for detecting keypoints in retinal images. However, its limitations lie in the dependency on a small initial set of ground truth keypoints, which is progressively expanded to detect more keypoints. Having encountered difficulties in detecting consistent initial keypoints in brain images using SIFT and LoFTR, we proposed a new approach: radiomic feature-based keypoint detection. Demonstrating the anatomical significance of the detected keypoints was achieved by showcasing their efficacy in improving registration processes guided by these keypoints. Subsequently, these keypoints were employed as the ground truth for the keypoint detection method (LK-SuperRetina). Furthermore, the study showcases the application of GNNs in image matching, highlighting their superior performance in terms of both the number of good matches and confidence scores. This research sets the stage for expanding GNN applications into various other applications, including but not limited to image classification, segmentation, and registration.
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