An Automatic Framework to Create Patient-specific Eye Models From 3D Magnetic Resonance Images for Treatment Selection in Patients With Uveal Melanoma

Mohamed Kilany Hassan; Emmanuelle Fleury; Denis Shamonin; Lorna Grech Fonk; Marina Marinkovic; Myriam G Jaarsma-Coes; Gregorius P M Luyten; Andrew Webb; Jan-Willem Beenakker; Berend Stoel

doi:10.1016/j.adro.2021.100697

An Automatic Framework to Create Patient-specific Eye Models From 3D Magnetic Resonance Images for Treatment Selection in Patients With Uveal Melanoma

Adv Radiat Oncol. 2021 Apr 3;6(6):100697. doi: 10.1016/j.adro.2021.100697. eCollection 2021 Nov-Dec.

Authors

Mohamed Kilany Hassan¹, Emmanuelle Fleury^{2

3}, Denis Shamonin¹, Lorna Grech Fonk^{1

4}, Marina Marinkovic⁴, Myriam G Jaarsma-Coes^{1

4}, Gregorius P M Luyten⁴, Andrew Webb¹, Jan-Willem Beenakker^{1

4}, Berend Stoel¹

Affiliations

¹ Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
² Department of Radiation Oncology, Erasmus Medical Center, Rotterdam, The Netherlands.
³ Department of Radiation Oncology, HollandPTC, Delft, The Netherlands.
⁴ Department of Ophthalmology, Leiden University Medical Center, Leiden, The Netherlands.

Abstract

Purpose: The optimal treatment strategy for uveal melanoma (UM) relies on many factors, the most important being tumor size and location. Building on recent developments in high-resolution 3D ocular magnetic resonance imaging (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.

Methods and materials: From 15 patients with UM, 3D inversion-recovery gradient-echo (T1-weighted) and 3D fat-suppressed spin-echo (T2-weighted) images were acquired with a 7T MRI scanner. First, the sclera and cornea were segmented from the T2-weighted image by mesh-fitting. The T1- and T2-weighted images were then coregistered. From the registered T1-weighted image, the lens, vitreous body, retinal detachment, and tumor were segmented. Fuzzy C-means clustering was used to differentiate the 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 coefficient (mean surface distance) of 97.7%±0.84% (0.17±0.11 mm) was achieved 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 86.0%±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.

Conclusions: The described framework combined information from T1- and T2-weighted images to accurately determine tumor boundaries in 3D. The proposed process may have a direct effect on clinical workflow, as it enables an accurate 3D assessment of tumor dimensions, which directly influences therapy selection.