Utilizing Radiomic Feature Analysis 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
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.