Enhancing Precision in Medical Imaging: A 3D CNN Approach for Fiducial Point Detection in MRI Data

The significance of fiducial marker detection in neuroimaging cannot be overstated, as these markers serve as vital reference points for accurate spatial alignment during image registration. Our proposed model addresses challenges in consistent marker placement and variability during MRI scanning, e...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2024, Vol.12, p.52086-52096
Main Authors: Suhas, M. V., Sinha, Sanjib, Mariyappa, N., Anitha, H., Kotegar, Karunakar A.
Format: Article
Language:English
Subjects:
3D MRI data
Accuracy
Algorithms
Artificial neural networks
Biomedical imaging
Convolutional neural networks
Feature extraction
Fiducial markers
Fiducial point detection
Image registration
Localization
Location awareness
Magnetic resonance imaging
Medical imaging
Neuroimaging
Three-dimensional displays
Training
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The significance of fiducial marker detection in neuroimaging cannot be overstated, as these markers serve as vital reference points for accurate spatial alignment during image registration. Our proposed model addresses challenges in consistent marker placement and variability during MRI scanning, ensuring reliable localization for subsequent analysis. This paper introduces an innovative approach to fiducial marker detection in T1-weighted MRI volumes, specifically targeting the Left Preauricular Point (LPA), Right Pre-auricular Point (RPA), and Nasion. The implementation employs a 3D Convolutional Neural Network (CNN) to achieve precise localization of these crucial anatomical landmarks. Operating on a high-performance system our algorithm demonstrated exceptional accuracy and sensitivity using MATLAB R2023a as the primary tool for development and evaluation. Rigorous experiments on a diverse dataset showcased the algorithm's robust performance. For RPA detection, the model achieved 96.55% accuracy, emphasizing sensitivity (96.78% recall) and precision (96.35%). LPA detection demonstrated an impressive accuracy of 96.88%, with heightened sensitivity (96.95%) and precision (96.83%). The nasion detection process exhibited precise localization, with a Mean Square Error (MSE) of 0.3439 for 36 volume data. These results highlight the algorithm's potential to enhance accuracy and efficiency in fiducial point detection for improved neuroimaging studies.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3385573