Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A Novel Statistical Shape Model for Treatment Planning of XXXX .

@entity1 @entity2194 @entity267 @entity12938 @entity5 @entity1347 @entity3457 @entity995
PURPOSE: Proper delineation of ocular anatomy in 3-dimensional (3D) imaging is a big challenge, particularly when developing treatment plans for @entity2194 . Magnetic resonance imaging (MRI) is presently used in clinical practice for diagnosis confirmation and treatment planning for treatment of @entity995 in @entity1 , where it serves as a source of information, complementary to the fundus or ultrasonographic imaging. Here we present a framework to fully automatically segment the eye anatomy for MRI based on 3D @entity3457 ( @entity3457 ), and we validate the results and present a proof of concept to automatically segment pathological eyes. METHODS AND MATERIALS: Manual and automatic segmentation were performed in 24 images of healthy @entity1 ' @entity12938 (3.29      2.15  years of age). Imaging was performed using a 3-T MRI scanner. The @entity3457 consists of the lens, the vitreous humor, the sclera, and the @entity267 . The model was fitted by first automatically detecting the position of the eye center, the lens, and the optic nerve, and then aligning the model and fitting it to the @entity1 . We validated our segmentation method by using a leave-one-out cross-validation. The segmentation results were evaluated by measuring the overlap, using the Dice similarity coefficient (DSC) and the mean distance error. RESULTS: We obtained a DSC of 94.90      2.12% for the sclera and the @entity267 , 94.72      1.89% for the vitreous humor, and 85.16      4.91% for the lens. The mean distance error was 0.26      0.09  mm. The entire process took 14  seconds on average per eye. CONCLUSION: We provide a reliable and accurate tool that enables clinicians to @entity1347 , the @entity267 , the vitreous humor, and the lens, using MRI. We additionally present a proof of concept for fully automatically segmenting eye pathology. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the @entity5 .
