A Systematic and Comparative Study in Deep Learning Approaches in Extraocular Muscle Segmentation and Analysis in Orbit Magnetic Resonance Images
| dc.contributor.advisor | Wei, Qi | |
| dc.contributor.author | Qureshi, Amad Aamir | |
| dc.date.accessioned | 2024-02-23T17:58:16Z | |
| dc.date.available | 2024-02-23T17:58:16Z | |
| dc.date.issued | 2023-08 | |
| dc.description.abstract | Strabismus is an ocular condition characterized by binocular misalignment, which impacts about 5% of the global population. It can cause double vision, reduced vision, and impair the quality of life. Accurate diagnosis and treatment planning often benefits from the anatomical evaluation of the extraocular muscles (EOMs) that can be obtained by imaging modalities, such as magnetic resonance imaging (MRI). Such image-based examination requires segmenting the ocular structures from images, which is a labor and time-intensive task, subject to error when done manually. Deep learning-based segmentation has shown promise to outline anatomical structures automatically and objectively. We performed three sets of experimentation for EOM segmentation via DLmethods. Furthermore, we analyzed the performance of the deep learning methods through F-measure-based metrics, intersection over union (IoU) and Dice coefficient, and estimation of the EOM centroid (centroid offset). We first investigated the performance of U-Net, U-NeXt, DeepLabV3+, and ConResNet in multi-class pixel-based segmentation of the EOMs from ocular MRI taken in the quasi-coronal plane. Based on the performance evaluation (visual and the quantitative metrics mentioned), the U-Net model achieved the highest overall segmentation accuracy, and lowest centroid offset. It was noted that segmentation accuracy varied in spatially different image plane – relative to the middle slice (optic nerve junction point) in the MRI stack. In the second set of experiments, we compared the performance of the U-Net model with its variants, UNeXt, Attention U-Net and FD-UNet and subjected the prediction outputs to the same evaluation as before, with U-Net achieving the best performance. We also explored methods in an attempt to improve the model performance – particularly with data augmentation and enhancement, where methods such as Adaptive Gamma Correction and CLAHE enhancement were used with the U-Net model. No significant difference was observed when CLAHE, Adaptive Gamma Correction and a dataset with unenhanced, CLAHE, and adaptive gamma corrected images were tested against unenhanced data, however, did result in better quantitative performance than the standard augmentation technique. Our study provides the insights into the factors that impact the accuracy of deep learning models in segmenting the EOMs, such as spatial slice location, image quality, and contrast and demonstrate the potential of these models in translating into 3D space for potential diagnosis and treatment planning for patients with strabismus and other ocular conditions. | |
| dc.format.medium | masters theses | |
| dc.identifier.uri | http://hdl.handle.net/1920/13551 | |
| dc.identifier.uri | https://doi.org/10.13021/MARS/2026 | |
| dc.language.iso | en_US | |
| dc.rights | Copyright 2023 Amad Aamir Qureshi | |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0 | |
| dc.subject | Deep Learning | |
| dc.subject | Strabismus | |
| dc.subject | Ophthalmology | |
| dc.subject | Extraocular Muscles | |
| dc.subject | MRI | |
| dc.subject | Segmentation | |
| dc.title | A Systematic and Comparative Study in Deep Learning Approaches in Extraocular Muscle Segmentation and Analysis in Orbit Magnetic Resonance Images | |
| dc.type | Thesis | |
| thesis.degree.discipline | Bioengineering | |
| thesis.degree.grantor | George Mason University | |
| thesis.degree.level | Master's | |
| thesis.degree.name | Master of Science in Bioengineering |