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

PURPOSE: Proper delineation of ocular anatomy in 3-dimensional (3D) imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic resonance imaging (MRI) is presently used in clinical practice for diagnosis confirmation and treatment planning for treatment of retinoblastoma in infants, 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 active shape models (ASM), 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 children's eyes (3.29 ± 2.15 years of age). Imaging was performed using a 3-T MRI scanner. The ASM consists of the lens, the vitreous humor, the sclera, and the cornea. 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 patient. 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 cornea, 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 automatically segment the sclera, the cornea, 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 tumor.

Noninvasive referencing of intraocular tumors for external beam radiation therapy using optical coherence tomography: a proof of concept.

PURPOSE: External beam radiation therapy is currently considered the most common treatment modality for intraocular tumors. Localization of the tumor and efficient compensation of tumor misalignment with respect to the radiation beam are crucial. According to the state of the art procedure, localization of the target volume is indirectly performed by the invasive surgical implantation of radiopaque clips or is limited to positioning the head using stereoscopic radiographies. This work represents a proof-of-concept for direct and noninvasive tumor referencing based on anterior eye topography acquired using optical coherence tomography (OCT). METHODS: A prototype of a head-mounted device has been developed for automatic monitoring of tumor position and orientation in the isocentric reference frame for LINAC based treatment of intraocular tumors. Noninvasive tumor referencing is performed with six degrees of freedom based on anterior eye topography acquired using OCT and registration of a statistical eye model. The proposed prototype was tested based on enucleated pig eyes and registration accuracy was measured by comparison of the resulting transformation with tilt and torsion angles manually induced using a custom-made test bench. RESULTS: Validation based on 12 enucleated pig eyes revealed an overall average registration error of 0.26 ± 0.08° in 87 ± 0.7 ms for tilting and 0.52 ± 0.03° in 94 ± 1.4 ms for torsion. Furthermore, dependency of sampling density on mean registration error was quantitatively assessed. CONCLUSIONS: The tumor referencing method presented in combination with the statistical eye model introduced in the past has the potential to enable noninvasive treatment and may improve quality, efficacy, and flexibility of external beam radiotherapy of intraocular tumors.

Statistical modeling of the eye for multimodal treatment planning for external beam radiation therapy of intraocular tumors.

PURPOSE: Ocular anatomy and radiation-associated toxicities provide unique challenges for external beam radiation therapy. For treatment planning, precise modeling of organs at risk and tumor volume are crucial. Development of a precise eye model and automatic adaptation of this model to patients' anatomy remain problematic because of organ shape variability. This work introduces the application of a 3-dimensional (3D) statistical shape model as a novel method for precise eye modeling for external beam radiation therapy of intraocular tumors. METHODS AND MATERIALS: Manual and automatic segmentations were compared for 17 patients, based on head computed tomography (CT) volume scans. A 3D statistical shape model of the cornea, lens, and sclera as well as of the optic disc position was developed. Furthermore, an active shape model was built to enable automatic fitting of the eye model to CT slice stacks. Cross-validation was performed based on leave-one-out tests for all training shapes by measuring dice coefficients and mean segmentation errors between automatic segmentation and manual segmentation by an expert. RESULTS: Cross-validation revealed a dice similarity of 95%±2% for the sclera and cornea and 91%±2% for the lens. Overall, mean segmentation error was found to be 0.3±0.1 mm. Average segmentation time was 14±2 s on a standard personal computer. CONCLUSIONS: Our results show that the solution presented outperforms state-of-the-art methods in terms of accuracy, reliability, and robustness. Moreover, the eye model shape as well as its variability is learned from a training set rather than by making shape assumptions (eg, as with the spherical or elliptical model). Therefore, the model appears to be capable of modeling nonspherically and nonelliptically shaped eyes.