Utilizing Radiomic Feature Analysis For Automated MRI Keypoint Detection: Enhancing Graph Applications

• URL: http://arxiv.org/abs/2311.18281v1
• Date: Thu, 30 Nov 2023 06:37:02 GMT
• Title: Utilizing Radiomic Feature Analysis For Automated MRI Keypoint Detection: Enhancing Graph Applications
• Authors: Sahar Almahfouz Nasser, Shashwat Pathak, Keshav Singhal, Mohit Meena, Nihar Gupte, Ananya Chinmaya, Prateek Garg, and Amit Sethi
• Abstract summary: Graph neural networks (GNNs) present a promising alternative to CNNs and transformers in certain image processing applications. One approach involves converting images into nodes by identifying significant keypoints within them. This research sets the stage for expanding GNN applications into various applications, including but not limited to image classification, segmentation, and registration.
• Score: 2.8084568003406316
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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