The optimal treatment for uveal melanoma, the most common primary malignant eye tumor, depends on tumor thickness. Conventionally tumor thickness is determined with 2D ultrasound, but MRI allows for a full 3D analysis. It is, however, often difficult to determine the maximum tumor thickness due to its complex 3D shape. We propose a fully automatic framework to segment these MR-images to measure the tumor thickness accurately and evaluate it in four patients. The proposed method has a direct impact on the clinical practice, as a more accurate 3D assessment of the tumor dimensions directly influences therapy determination.