An automatic framework to create patient-specific eye models from 3D MR-images for treatment selection in patients with uveal melanoma

Abstract Purpose: The optimal treatment strategy for Uveal Melanoma (UM) relies on many factors, the most important one being tumor size and location. Building upon recent developments in high-resolution 3D ocular-MRI, we developed an automatic image processing framework to create patient-specific eye models, and to subsequently determine the full 3D tumor shape and size automatically. Materials and Methods: From UM 15 patients, 3D inversion-recovery gradient-echo (T1-weighted) and 3D fat-suppressed spin-echo (T2-weighted) images were acquired with a 7T MRI. First, the sclera and cornea were segmented from the T2-weighted image by mesh-fitting. The T1- and T2-weighted images were then co-registered. From the registered T1-weighted image, the lens, vitreous body, retinal detachment and tumor were segmented. Fuzzy C-means clustering was used to differentiate tumor from retinal detachments. The tumor model was verified and (if needed) edited by an ophthalmic MRI specialist. Subsequently, the prominence and largest basal diameter of the tumor were measured automatically based on the verified contours. These results were compared with manual assessments on the original images, and with ultrasound measurements, to show the errors in manual analysis. Results: The framework successfully created an eye model fully automatically in 12 cases. In these cases, a Dice similarity (mean surface distance) was achieved of 97.7±0.84% (0.17±0.11 mm) for the sclera, 96.8±1.05% (0.20±0.06 mm) for the vitreous body, 91.6±4.83% (0.15±0.06 mm) for the lens, and 86±7.4% (0.35±0.27 mm) for the tumor. The manual assessments deviated, on average, 0.39±0.31 mm in prominence and 1.7±1.22 mm in basal diameter from the automatic measurements. Conclusion: The described framework combines information from T1- and T2-weighted images to accurately determine tumor boundaries in 3D. The proposed work can have a direct impact on clinical workflow, as it enables an accurate 3D assessment of tumor dimensions, which directly influences therapy selection.