The purpose of this study is to understand the phenotypes of thyroid eye disease (TED) through data derived from a multiatlas segmentation of computed tomography (CT) imaging. Images of 170 orbits of 85 retrospectively selected TED patients were analyzed with the developed automated segmentation tool. Twenty-five bilateral orbital structural metrics were used to perform principal component analysis (PCA). PCA of the 25 structural metrics identified the two most dominant structural phenotypes or characteristics, the “big volume phenotype” and the “stretched optic nerve phenotype,” that accounted for 60% of the variance. Most of the subjects in the study have either of these characteristics or a combination of both. A Kendall rank correlation between the principal components (phenotypes) and clinical data showed that the big volume phenotype was very strongly correlated (p-value <0.05) with motility defects, and loss of visual acuity. Whereas, the stretched optic nerve phenotype was strongly correlated (p-value <0.05) with an increased Hertel measurement, relatively better visual acuity, and smoking. Two clinical subtypes of TED, type 1 with enlarged muscles and type 2 with proptosis, are recognizable in CT imaging. Our automated algorithm identifies the phenotypes and finds associations with clinical markers.
We examine imaging and electronic medical records (EMR) of 588 subjects over five major disease groups that affect optic nerve function. An objective evaluation of the role of imaging and EMR data in diagnosis of these conditions would improve understanding of these diseases and help in early intervention. We developed an automated image processing pipeline that identifies the orbital structures within the human eyes from computed tomography (CT) scans, calculates structural size, and performs volume measurements. We customized the EMR-based phenome-wide association study (PheWAS) to derive diagnostic EMR phenotypes that occur at least two years prior to the onset of the conditions of interest from a separate cohort of 28,411 ophthalmology patients. We used random forest classifiers to evaluate the predictive power of image-derived markers, EMR phenotypes, and clinical visual assessments in identifying disease cohorts from a control group of 763 patients without optic nerve disease. Image-derived markers showed more predictive power than clinical visual assessments or EMR phenotypes. However, the addition of EMR phenotypes to the imaging markers improves the classification accuracy against controls: the AUC improved from 0.67 to 0.88 for glaucoma, 0.73 to 0.78 for intrinsic optic nerve disease, 0.72 to 0.76 for optic nerve edema, 0.72 to 0.77 for orbital inflammation, and 0.81 to 0.85 for thyroid eye disease. This study illustrates the importance of diagnostic context for interpretation of image-derived markers and the proposed PheWAS technique provides a flexible approach for learning salient features of patient history and incorporating these data into traditional machine learning analyses.
Eye diseases and visual impairment affect millions of Americans and induce billions of dollars in annual economic
burdens. Expounding upon existing knowledge of eye diseases could lead to improved treatment and disease prevention.
This research investigated the relationship between structural metrics of the eye orbit and visual function measurements
in a cohort of 470 patients from a retrospective study of ophthalmology records for patients (with thyroid eye disease,
orbital inflammation, optic nerve edema, glaucoma, intrinsic optic nerve disease), clinical imaging, and visual function
assessments. Orbital magnetic resonance imaging (MRI) and computed tomography (CT) images were retrieved and
labeled in 3D using multi-atlas label fusion. Based on the 3D structures, both traditional radiology measures (e.g., Barrett
index, volumetric crowding index, optic nerve length) and novel volumetric metrics were computed. Using stepwise
regression, the associations between structural metrics and visual field scores (visual acuity, functional acuity, visual
field, functional field, and functional vision) were assessed. Across all models, the explained variance was reasonable
(R2 ~ 0.1-0.2) but highly significant (p < 0.001). Instead of analyzing a specific pathology, this study aimed to analyze
data across a variety of pathologies. This approach yielded a general model for the connection between orbital structural
imaging biomarkers and visual function.
KEYWORDS: Optic nerve, Magnetic resonance imaging, Visualization, Information visualization, Retina, Nerve, Image segmentation, 3D modeling, Radio optics, Statistical analysis
The optic nerve (ON) is a vital structure in the human visual system and transports all visual information from the retina
to the cortex for higher order processing. Due to the lack of redundancy in the visual pathway, measures of ON damage
have been shown to correlate well with visual deficits. These measures are typically taken at an arbitrary anatomically
defined point along the nerve and do not characterize changes along the length of the ON. We propose a fully automated,
three-dimensionally consistent technique building upon a previous independent slice-wise technique to estimate the radius
of the ON and surrounding cerebrospinal fluid (CSF) on high-resolution heavily T2-weighted isotropic MRI. We show
that by constraining results to be three-dimensionally consistent this technique produces more anatomically viable results.
We compare this technique with the previously published slice-wise technique using a short-term reproducibility data set,
10 subjects, follow-up <1 month, and show that the new method is more reproducible in the center of the ON. The center
of the ON contains the most accurate imaging because it lacks confounders such as motion and frontal lobe interference.
Long-term reproducibility, 5 subjects, follow-up of approximately 11 months, is also investigated with this new technique
and shown to be similar to short-term reproducibility, indicating that the ON does not change substantially within 11
months. The increased accuracy of this new technique provides increased power when searching for anatomical changes
in ON size amongst patient populations.
KEYWORDS: Nerve, Control systems, Information visualization, Optic nerve, Visualization, 3D image processing, Image segmentation, Radio optics, Tissue optics, Tissues
The optic nerve (ON) plays a crucial role in human vision transporting all visual information from the retina to the brain for higher order processing. There are many diseases that affect the ON structure such as optic neuritis, anterior ischemic optic neuropathy and multiple sclerosis. Because the ON is the sole pathway for visual information from the retina to areas of higher level processing, measures of ON damage have been shown to correlate well with visual deficits. Increased intracranial pressure has been shown to correlate with the size of the cerebrospinal fluid (CSF) surrounding the ON. These measures are generally taken at an arbitrary point along the nerve and do not account for changes along the length of the ON. We propose a high contrast and high-resolution 3-D acquired isotropic imaging sequence optimized for ON imaging. We have acquired scan-rescan data using the optimized sequence and a current standard of care protocol for 10 subjects. We show that this sequence has superior contrast-to-noise ratio to the current standard of care while achieving a factor of 11 higher resolution. We apply a previously published automatic pipeline to segment the ON and CSF sheath and measure the size of each individually. We show that these measures of ON size have lower short- term reproducibility than the population variance and the variability along the length of the nerve. We find that the proposed imaging protocol is (1) useful in detecting population differences and local changes and (2) a promising tool for investigating biomarkers related to structural changes of the ON.
Pathologies of the optic nerve and orbit impact millions of Americans and quantitative assessment of the orbital structures on 3-D imaging would provide objective markers to enhance diagnostic accuracy, improve timely intervention, and eventually preserve visual function. Recent studies have shown that the multi-atlas methodology is suitable for identifying orbital structures, but challenges arise in the identification of the individual extraocular rectus muscles that control eye movement. This is increasingly problematic in diseased eyes, where these muscles often appear to fuse at the back of the orbit (at the resolution of clinical computed tomography imaging) due to inflammation or crowding. We propose the use of Kalman filters to track the muscles in three-dimensions to refine multi-atlas segmentation and resolve ambiguity due to imaging resolution, noise, and artifacts. The purpose of our study is to investigate a method of automatically generating orbital metrics from CT imaging and demonstrate the utility of the approach by correlating structural metrics of the eye orbit with clinical data and visual function measures in subjects with thyroid eye disease. The pilot study demonstrates that automatically calculated orbital metrics are strongly correlated with several clinical characteristics. Moreover, it is shown that the superior, inferior, medial and lateral rectus muscles obtained using Kalman filters are each correlated with different categories of functional deficit. These findings serve as foundation for further investigation in the use of CT imaging in the study, analysis and diagnosis of ocular diseases, specifically thyroid eye disease.
Optic neuritis is a sudden inflammation of the optic nerve (ON) and is marked by pain on eye movement, and visual symptoms such as a decrease in visual acuity, color vision, contrast and visual field defects. The ON is closely linked with multiple sclerosis (MS) and patients have a 50% chance of developing MS within 15 years. Recent advances in multi-atlas segmentation methods have omitted volumetric assessment. In the past, measuring the size of the ON has been done by hand. We utilize a new method of automatically segmenting the ON to measure the radii of both the ON and surrounding cerebrospinal fluid (CSF) sheath to develop a normative distribution of healthy young adults. We examine this distribution for any trends and find that ON and CSF sheath radii do not vary between 20-35 years of age and between sexes. We evaluate how six patients suffering from optic neuropathy compare to this distribution of controls. We find that of these six patients, five of them qualitatively differ from the normative distribution which suggests this technique could be used in the future to distinguish between optic neuritis patients and healthy controls
The optic nerve (ON) plays a critical role in many devastating pathological conditions. Segmentation of the ON has the ability to provide understanding of anatomical development and progression of diseases of the ON. Recently, methods have been proposed to segment the ON but progress toward full automation has been limited. We optimize registration and fusion methods for a new multi-atlas framework for automated segmentation of the ONs, eye globes, and muscles on clinically acquired computed tomography (CT) data. Briefly, the multi-atlas approach consists of determining a region of interest within each scan using affine registration, followed by nonrigid registration on reduced field of view atlases, and performing statistical fusion on the results. We evaluate the robustness of the approach by segmenting the ON structure in 501 clinically acquired CT scan volumes obtained from 183 subjects from a thyroid eye disease patient population. A subset of 30 scan volumes was manually labeled to assess accuracy and guide method choice. Of the 18 compared methods, the ANTS Symmetric Normalization registration and nonlocal spatial simultaneous truth and performance level estimation statistical fusion resulted in the best overall performance, resulting in a median Dice similarity coefficient of 0.77, which is comparable with inter-rater (human) reproducibility at 0.73.
KEYWORDS: Image segmentation, Optic nerve, Magnetic resonance imaging, Eye, In vivo imaging, Error analysis, Image fusion, Biomedical optics, Magnetorheological finishing, Control systems
Multiatlas methods have been successful for brain segmentation, but their application to smaller anatomies remains relatively unexplored. We evaluate seven statistical and voting-based label fusion algorithms (and six additional variants) to segment the optic nerves, eye globes, and chiasm. For nonlocal simultaneous truth and performance level estimation (STAPLE), we evaluate different intensity similarity measures (including mean square difference, locally normalized cross-correlation, and a hybrid approach). Each algorithm is evaluated in terms of the Dice overlap and symmetric surface distance metrics. Finally, we evaluate refinement of label fusion results using a learning-based correction method for consistent bias correction and Markov random field regularization. The multiatlas labeling pipelines were evaluated on a cohort of 35 subjects including both healthy controls and patients. Across all three structures, nonlocal spatial STAPLE (NLSS) with a mixed weighting type provided the most consistent results; for the optic nerve NLSS resulted in a median Dice similarity coefficient of 0.81, mean surface distance of 0.41 mm, and Hausdorff distance 2.18 mm for the optic nerves. Joint label fusion resulted in slightly superior median performance for the optic nerves (0.82, 0.39 mm, and 2.15 mm), but slightly worse on the globes. The fully automated multiatlas labeling approach provides robust segmentations of orbital structures on magnetic resonance imaging even in patients for whom significant atrophy (optic nerve head drusen) or inflammation (multiple sclerosis) is present.
The optic nerve is a sensitive central nervous system structure, which plays a critical role in many devastating pathological conditions. Several methods have been proposed in recent years to segment the optic nerve automatically, but progress toward full automation has been limited. Multi-atlas methods have been successful for brain segmentation, but their application to smaller anatomies remains relatively unexplored. Herein we evaluate a framework for robust and fully automated segmentation of the optic nerves, eye globes and muscles. We employ a robust registration procedure for accurate registrations, variable voxel resolution and image fieldof- view. We demonstrate the efficacy of an optimal combination of SyN registration and a recently proposed label fusion algorithm (Non-local Spatial STAPLE) that accounts for small-scale errors in registration correspondence. On a dataset containing 30 highly varying computed tomography (CT) images of the human brain, the optimal registration and label fusion pipeline resulted in a median Dice similarity coefficient of 0.77, symmetric mean surface distance error of 0.55 mm, symmetric Hausdorff distance error of 3.33 mm for the optic nerves. Simultaneously, we demonstrate the robustness of the optimal algorithm by segmenting the optic nerve structure in 316 CT scans obtained from 182 subjects from a thyroid eye disease (TED) patient population.
Current pharmacological therapies for the treatment of chronic optic neuropathies such as glaucoma are often inadequate due to their inability to directly affect the optic nerve and prevent neuron death. While drugs that target the neurons have been developed, existing methods of administration are not capable of delivering an effective dose of medication along the entire length of the nerve. We have developed an image-guided system that utilizes a magnetically tracked flexible endoscope to navigate to the back of the eye and administer therapy directly to the optic nerve. We demonstrate the capabilities of this system with a series of targeted surgical interventions in the orbits of live pigs. Target objects consisted of NMR microspherical bulbs with a volume of 18 μL filled with either water or diluted gadolinium-based contrast, and prepared with either the presence or absence of a visible coloring agent. A total of 6 pigs were placed under general anesthesia and two microspheres of differing color and contrast content were blindly implanted in the fat tissue of each orbit. The pigs were scanned with T1-weighted MRI, image volumes were registered, and the microsphere containing gadolinium contrast was designated as the target. The surgeon was required to navigate the flexible endoscope to the target and identify it by color. For the last three pigs, a 2D/3D registration was performed such that the target's coordinates in the image volume was noted and its location on the video stream was displayed with a crosshair to aid in navigation. The surgeon was able to correctly identify the target by color, with an average intervention time of 20 minutes for the first three pigs and 3 minutes for the last three.
Labeling or segmentation of structures of interest on medical images plays an essential role in both clinical and scientific
understanding of the biological etiology, progression, and recurrence of pathological disorders. Here, we focus on the
optic nerve, a structure that plays a critical role in many devastating pathological conditions – including glaucoma,
ischemic neuropathy, optic neuritis and multiple-sclerosis. Ideally, existing fully automated procedures would result in
accurate and robust segmentation of the optic nerve anatomy. However, current segmentation procedures often require
manual intervention due to anatomical and imaging variability. Herein, we propose a framework for robust and fully-automated
segmentation of the optic nerve anatomy. First, we provide a robust registration procedure that results in
consistent registrations, despite highly varying data in terms of voxel resolution and image field-of-view. Additionally,
we demonstrate the efficacy of a recently proposed non-local label fusion algorithm that accounts for small scale errors
in registration correspondence. On a dataset consisting of 31 highly varying computed tomography (CT) images of the
human brain, we demonstrate that the proposed framework consistently results in accurate segmentations. In particular,
we show (1) that the proposed registration procedure results in robust registrations of the optic nerve anatomy, and (2)
that the non-local statistical fusion algorithm significantly outperforms several of the state-of-the-art label fusion
algorithms.
We have developed a combined image-guided and minimally invasive system for the delivery of therapy to the back of
the eye. It is composed of a short 4.5 mm diameter endoscope with a magnetic tracker embedded in the tip. In previous
work we have defined an optimized fiducial placement for accurate guidance to the back of the eye and are now moving
to system testing.
The fundamental difficulty in testing performance is establishing a target in a manner which closely mimics the
physiological task. We have to have a penetrable material which obscures line of sight, similar to the orbital fat. In
addition we need to have some independent measure of knowing when a target has been reached to compare to the ideal
performance. Lastly, the target cannot be rigidly attached to the skull phantom since the optic nerve lies buried in the
orbital fat.
We have developed a skull phantom with white cloth stellate balls supporting a correctly sized globe. Placed in
the white balls are red, blue, orange and yellow balls. One of the colored balls has been soaked in barium to make it
bright on CT. The user guides the tracked endoscope to the target as defined by the images and tells us its color. We
record task accuracy and time to target. We have tested this with 28 residents, fellows and attending physicians. Each
physician performs the task twice guided and twice unguided. Results will be presented.
Optic neuropathies are historically difficult to treat due to difficulty in reaching the optic nerve in the tight neurovascular environment of the orbit. An orbital endoscopic system is currently under development that may be able to administer treatment to the optic nerve in a significantly less invasive manner than that of previous surgical procedures. However, due the tight confines of the orbital environment, this endoscopic system has proven to be time consuming and tedious. By combining an orbital endoscope with a flexible electromagnetic tracking system, it may be possible to develop a quick and accurate method of locating the optic nerve. Much of the guesswork involved in orbital endoscopy could be relieved by live tracking in preoperative CT scans. This project focuses on the accuracy assessment of such a tracked endoscopic system as well as its implementation in phantom and animal models. With the combined benefits of orbital endoscopy and electromagnetic tracking, this is the route to providing the best support possible for safe and efficient orbital surgery.
Our previous studies using rabbits and monkeys showed that the Amide II wavelength (6.45 micrometers ) produced by the FEL could efficiently produce an optic nerve sheath fenestration with minimal damage. In order to determine if the technology safely could be applied to human surgery, we used 2 blind human eyes during enucleation to compare the results of producing fenestrations with the FEL or a scissors. FDA and Vanderbilt IRB approvals, and individual patient consents were obtained. The FEL energy was transmitted to a human operating room. After disinsertion of the medial rectus muscle, an optic nerve sheath fenestration (2 mm diameter) was made with either the FEL (6.45 micrometers , 325 micrometers spot size, 30 Hz, 3 mJ) through a hollow waveguide surgical probe or with a scissors. The enucleation was then completed. The optic nerve was dissected from the globe and fixed. Specimens were examined histologically. Dural incisions were effective with both methods. FEL energy at 6.45 micrometers can be transmitted to an operating room and delivered to human ocular tissue through a hollow waveguide surgical probe. This FEL wavelength can produce an optic nerve sheath fenestration without acute direct damage to the nerve in this case report.
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