SegR3D: A Multi-Target 3D Visualization System for Realistic Volume Rendering of Meningiomas

Meningiomas are the most common primary intracranial tumors in adults. Surgical resection of most meningiomas is effective in reducing recurrence risk. Visualization of meningiomas helps radiologists assess the distribution and volume of the tumor within the brain and facilitates planning the surgery by neurosurgeons. This paper introduces an innovative realistic 3D medical visualization system, namely SegR3D. It incorporates a 3D medical image segmentation pipeline, which preprocesses the data via semi-supervised learning-based multi-target segmentation to generate masks of the lesion areas. Subsequently, both the original medical images and segmentation masks are utilized as non-scalar volume data inputs into the realistic rendering pipeline. We propose a novel importance transfer function, assigning varying degrees of importance to different mask values to emphasize the areas of interest. Our rendering pipeline integrates physically based rendering with advanced illumination techniques to enhance the depiction of the structural characteristics and shapes of lesion areas. We conducted a user study involving medical practitioners to evaluate the effectiveness of SegR3D. Our experimental results indicate that SegR3D demonstrates superior efficacy in the visual analysis of meningiomas compared to conventional visualization methods.