Utilizing Radiomic Features for Automated MRI Keypoint Detection: Enhancing Graph Applications

Sahar Almahfouz Nasser, Shashwat Pathak, Keshav Singhal, Mohit Meena, Nihar Gupte, Ananya Chinmaya, Prateek Garg, Amit Sethi

Abstract: Graph neural networks (GNNs) present a promising alternative to CNNs and transformers for certain image processing applications due to their parameter-efficiency in modeling spatial relationships. Currently, an active area of research is to convert image data into graph data as input for GNN-based models. A natural choice for graph vertices, for instance, are keypoints in images. SuperRetina is a promising semi-supervised technique 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. We encountered difficulties in detecting a consistent set of initial keypoints in brain images using traditional keypoint detection techniques, such as SIFT and LoFTR. Therefore, we propose a new approach for detecting the initial keypoints for SuperRetina, which is based on radiomic features. We demonstrate the anatomical significance of the detected keypoints