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Structural Functional Associations of the Orbit in Thyroid Eye Disease: Kalman Filters to Track Extraocular Muscles

Posted by on Wednesday, February 10, 2016 in Computed Tomography, Eye Imaging, Image Segmentation.

Shikha Chaganti, Katrina Nelson, Kevin Mundy, Yifu Luo, Robert L. Harrigan, Steve Damon, Daniel Fabbri, Louise Mawn, Bennett Landman. “Structural Functional Associations of the Orbit in Thyroid Eye Disease: Kalman Filters to Track Extraocular Muscles”. In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Oral presentation.

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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, 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.

Muscle tracking using Kalman filters: (A) Input CT scan (B) output of label fusion (C) distance transform with imposed maxima (D) measured muscle pieces (E) Coronal view of muscles with respect to landmark (F) the resultant five Kalman filters, for each eye, tracking the muscles. Note that the muscles are well-separated in the front of the orbit (B), but as we approach the back of the orbit there is no longer a clean boundary between them due to inflammation and crowding. The measured positions at each slice are given by a watershed calculation as shown in (C) and segmented in (D). A distance function is calculated over slice z wherein the value of each pixel is given by the distance to its nearest non-zero pixel as shown in (C), creating a contour where there is a maxima at the center of each of the four muscles. At each slice, the algorithm examines the top, bottom, right, and left quadrants for each of the four muscles as see in (E). In total, ten Kalman filters (5 for each eye) are used to track the muscles (F), which results in the 3-D tracks shown in (F).


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