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

## TL;DR

SegR3D is a 3D visualization system that improves the realistic rendering of meningiomas to aid in diagnosis and surgical planning.

## Contribution

SegR3D introduces a novel 3D visualization system with a multi-target segmentation pipeline and an importance transfer function for meningioma analysis.

## Key findings

- SegR3D uses semi-supervised learning for accurate lesion segmentation in medical images.
- The system's realistic rendering pipeline enhances the visualization of meningioma structural features.
- User studies show SegR3D outperforms conventional methods in visual analysis of meningiomas.

## Abstract

Meningiomas are the most common primary intracranial tumors in adults. For most cases, surgical resection is effective in mitigating recurrence risk. Accurate visualization of meningiomas helps radiologists assess the distribution and volume of the tumor within the brain while assisting neurosurgeons in preoperative planning. 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.