Using AI to Quantitatively Analyze Extraocular Muscle Pulley Morphology from MRI

Strabismus is the misalignment of the eyes, which can be caused by abnormalities in the pulley connective tissues. Magnetic Resonance Imaging (MRI) is used to study the structural and biomechanical features of strabismus. Recently, artificial intelligence (AI) techniques, specifically deep learning, have been implemented in segmenting the images obtained by MRI. However, these techniques need improvements for more accurate depictions of the structures. MRI images from patients with strabismus containing the extraocular muscle pulley morphology were obtained. Deep learning techniques will be used to locate the muscles using pixel-based labeling. Then, segmentation masks will be created containing the muscles with various colors. These locations will be analyzed through two F-measure-based metrics. These metrics are the Intersection of Union (IoU) and the Dice coefficient. While results have not yet been determined, the techniques are expected to accurately show the location of each extraocular muscle through different colors representing each muscle. These techniques are also expected to obtain IoU and Dice scores of close to 1 to show a complete overlap between the original MRI image and the image of the predicted locations. These refinements in the segmentation process will significantly enhance the performance of AI segmentation used for studying strabismus in medical imaging.

Hello everyone. My name is Jahayra Guzman-Rivas, and I am a bioengineering student at George Mason University. Today, I will be talking about my research in using artificial intelligence to quantitatively analyze the extraocular pulley morphology from MRI.

It is important to first understand Strabismus for this research project. Strabismus is the misalignment of the eyes. This condition occurs in 0.5 to 5 percent of the global population. Strabismus can be caused by abnormalities in any of the six extraocular muscles and their pulley systems. These five muscles are the medial rectus, lateral rectus, inferior rectus, superior rectus, and superior oblique.

When strabismus is examined in patients, magnetic resonance imaging, or MRI, has been implemented in the clinical spaces as it looks at the neuro-biomechanical factors of eye movements.

However, there are limitations to the use of MRI. When clinicians and trained experts segment the extraocular muscles and other ocular structures manually, it can be very time-consuming and labor-intensive.

In recent years, a specific field of study in Artificial Intelligence, specifically deep learning, has been applied to the process of segmenting the muscles in the eyes. Deep learning is a method in AI that instructs computers how to process data using neural networks. However, these techniques must be improved.

My research involves using deep learning methods to locate extraocular muscles using pixel-based labeling. I will be using MATLAB to implement deep learning methods. I will also use data collected from 13 patients. This data was collected at UCLA and intended for research purposes only.

Before I started using the deep learning methods, I conducted extensive literature reviews to further understand the anatomy of the eye and the utilization of deep learning methods.

I also confirmed which data is available and noted them in a summary sheet.

After noting the available data, I started preparing them for the deep learning methods. I looked at the images for each slice of each muscle for each eye for each patient and renamed them according to their slice and muscle using ImageJ. I then compiled all the slices of all the muscles of each eye of each patient in one folder. This process took about one month as I had 13 patients and 1,662 images to look at.

Since the code I obtained to create the masks required the slices for each eye for each patient to have a different naming format, I had to create new code in MATLAB that organized them into the right format. This took me about a week to complete.

I then used these stacks of images to create masks of the five muscles and have them shown in various colors.

As for the next steps, I must implement them into the deep learning model to train it with masks for each patient for each eye, validate the model, test the model, and adjust the model as needed.

While I made a lot of progress on my project, I could not complete it within this semester. However, I was able to gain a lot from this experience. For example, I was able to enhance my coding skills with MATLAB. Additionally, I gained a better understanding of deep learning algorithms and their implementation in segmentation. I also learned that preprocessing the data before implementing the deep learning methods are critical to the model’s training process.

I want to express many thanks to Dr. Lee and the George Mason University Office of Student Creative Activities and Research as they helped fund this research through the Undergraduate Research Scholars Program. I also want to thank my mentor, Dr. Wei, for guiding me throughout this process. I want to acknowledge Amad Quereshi for guiding me and providing the code needed for my research.

Thank you!