摘要
.Abstracting eye models from MRI images is critical in advancing medical imaging, particularly for clinical diagnostics. Current methods often struggle with accuracy and efficiency, highlighting a gap this research aims to fill. This study investigates the application of machine learning methods, focusing on the U-net-based deep learning framework, to improve the accuracy of eye model extraction. The objectives include fitting measured eye data to models such as the Ellipsoid model, evaluating automated segmentation tools, and assessing the usability of machine learning-based extractions in clinical scenarios. We employed point cloud data of 202,872 points to fit eye models using ellipsoid, non-linear, and spherical fitting techniques. The fitting processes were optimized to ensure precision and reliability. We compared the performance of these models using mean squared error (MSE) as the primary metric. The non-linear model emerged as the most accurate, with a significantly lower MSE (1.186562) compared to the ellipsoid (781.0542) and spherical models. This finding indicates that the non-linear model provides a more detailed and precise representation of the eye’s geometry. These results suggest that machine learning methods, particularly non-linear models, can significantly enhance the accuracy and usability of eye model extraction in clinical diagnostics, offering a robust framework for future advancements in medical imaging.
.Abstracting eye models from MRI images is critical in advancing medical imaging, particularly for clinical diagnostics. Current methods often struggle with accuracy and efficiency, highlighting a gap this research aims to fill. This study investigates the application of machine learning methods, focusing on the U-net-based deep learning framework, to improve the accuracy of eye model extraction. The objectives include fitting measured eye data to models such as the Ellipsoid model, evaluating automated segmentation tools, and assessing the usability of machine learning-based extractions in clinical scenarios. We employed point cloud data of 202,872 points to fit eye models using ellipsoid, non-linear, and spherical fitting techniques. The fitting processes were optimized to ensure precision and reliability. We compared the performance of these models using mean squared error (MSE) as the primary metric. The non-linear model emerged as the most accurate, with a significantly lower MSE (1.186562) compared to the ellipsoid (781.0542) and spherical models. This finding indicates that the non-linear model provides a more detailed and precise representation of the eye’s geometry. These results suggest that machine learning methods, particularly non-linear models, can significantly enhance the accuracy and usability of eye model extraction in clinical diagnostics, offering a robust framework for future advancements in medical imaging.
作者
Waqar Ahmad Tahir
Opeyemi Sheu Alamu
Dip Sarker
Md Tafhimul Haque Sadi
Abdullah Al Hasib
Tonmoy Kumar Sarker
Md Rakibul Islam
Dipankar Roy Dip
Ashish Sharma
Syed Wajeeh Abbas Rizvi
Waqar Ahmad Tahir;Opeyemi Sheu Alamu;Dip Sarker;Md Tafhimul Haque Sadi;Abdullah Al Hasib;Tonmoy Kumar Sarker;Md Rakibul Islam;Dipankar Roy Dip;Ashish Sharma;Syed Wajeeh Abbas Rizvi(Department of Internal Medicine/Casualty, Khalifa Gull Nawaz Teaching Hospital, Bannu, Pakistan;Department of Statistics, Federal College of Animal Health and Production Technology, Ibadan, Nigeria;Department of Computer Science, American International University-Bangladesh, Dhaka, Bangladesh;Department of Internal Medicine, Arogya Hospital Pvt. Ltd., Baglung, Nepal;Department of Data Science, National University of Computer & Emerging Sciences (FAST City Campus), Karachi, Pakistan)
