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Communication

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

1
School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
2
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
3
Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, Shanghai 200040, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
J. Imaging 2025, 11(7), 216; https://doi.org/10.3390/jimaging11070216
Submission received: 3 June 2025 / Revised: 27 June 2025 / Accepted: 27 June 2025 / Published: 30 June 2025
(This article belongs to the Section Visualization and Computer Graphics)

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.
Keywords: medical visualization; semi-supervised learning; image segmentation; realistic volume rendering medical visualization; semi-supervised learning; image segmentation; realistic volume rendering

Share and Cite

MDPI and ACS Style

Zhang, J.; Xu, C.; Xu, X.; Zhao, Y.; Zhao, L. SegR3D: A Multi-Target 3D Visualization System for Realistic Volume Rendering of Meningiomas. J. Imaging 2025, 11, 216. https://doi.org/10.3390/jimaging11070216

AMA Style

Zhang J, Xu C, Xu X, Zhao Y, Zhao L. SegR3D: A Multi-Target 3D Visualization System for Realistic Volume Rendering of Meningiomas. Journal of Imaging. 2025; 11(7):216. https://doi.org/10.3390/jimaging11070216

Chicago/Turabian Style

Zhang, Jiatian, Chunxiao Xu, Xinran Xu, Yajing Zhao, and Lingxiao Zhao. 2025. "SegR3D: A Multi-Target 3D Visualization System for Realistic Volume Rendering of Meningiomas" Journal of Imaging 11, no. 7: 216. https://doi.org/10.3390/jimaging11070216

APA Style

Zhang, J., Xu, C., Xu, X., Zhao, Y., & Zhao, L. (2025). SegR3D: A Multi-Target 3D Visualization System for Realistic Volume Rendering of Meningiomas. Journal of Imaging, 11(7), 216. https://doi.org/10.3390/jimaging11070216

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