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

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

2024

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 by showcasing their efficacy in improving image registration guided by these keypoints. We also employed these keypoints as ground truth for a modified keypoint detection method known as LK-SuperRetina for improved image matching in terms of both the number of matches and their confidence scores.

Paper Citation

in Harvard Style

Nasser S., Pathak S., Singhal K., Meena M., Gupte N., Chinmaya A., Garg P. and Sethi A. (2024). Utilizing Radiomic Features for Automated MRI Keypoint Detection: Enhancing Graph Applications. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING; ISBN 978-989-758-688-0, SciTePress, pages 319-325. DOI: 10.5220/0012568800003657

in Bibtex Style

@conference{bioimaging24,
author={Sahar Nasser and Shashwat Pathak and Keshav Singhal and Mohit Meena and Nihar Gupte and Ananya Chinmaya and Prateek Garg and Amit Sethi},
title={Utilizing Radiomic Features for Automated MRI Keypoint Detection: Enhancing Graph Applications},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING},
year={2024},
pages={319-325},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012568800003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING
TI - Utilizing Radiomic Features for Automated MRI Keypoint Detection: Enhancing Graph Applications
SN - 978-989-758-688-0
AU - Nasser S.
AU - Pathak S.
AU - Singhal K.
AU - Meena M.
AU - Gupte N.
AU - Chinmaya A.
AU - Garg P.
AU - Sethi A.
PY - 2024
SP - 319
EP - 325
DO - 10.5220/0012568800003657
PB - SciTePress