Evaluating the effect of voxel size on the accuracy of 3D volumetric analysis measurements of brain tumors
IntroductionNeurofibromatosis type 2 related Schwannomatosis (NF2-SWN) is a genetic disorder characterized by the growth of vestibular schwannomas (VS), which often leads to progressive hearing loss and vestibular dysfunction. Accurate volumetric assessment of VS tumors is crucial for effective moni...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Radiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fradi.2025.1618261/full |
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| author | Rithvik S. Ghankot Manwi Singh Shelby T. Desroches Noemi Jester Amit Mahajan Samantha Lorr Frank D. Buono Daniel H. Wiznia Daniel H. Wiznia Michele H. Johnson Steven M. Tommasini Steven M. Tommasini |
| author_facet | Rithvik S. Ghankot Manwi Singh Shelby T. Desroches Noemi Jester Amit Mahajan Samantha Lorr Frank D. Buono Daniel H. Wiznia Daniel H. Wiznia Michele H. Johnson Steven M. Tommasini Steven M. Tommasini |
| author_sort | Rithvik S. Ghankot |
| collection | DOAJ |
| description | IntroductionNeurofibromatosis type 2 related Schwannomatosis (NF2-SWN) is a genetic disorder characterized by the growth of vestibular schwannomas (VS), which often leads to progressive hearing loss and vestibular dysfunction. Accurate volumetric assessment of VS tumors is crucial for effective monitoring and treatment planning. Since tumor growth dynamics are often subtle, the resolution of MRI scans plays a critical role in detecting small volumetric changes that inform clinical decisions. This study evaluates the impact of MRI voxel resolution on the accuracy of manual and AI-driven volumetric segmentation of VS in NF2-SWN patients.MethodsTen patients with NF2-SWN, totaling 17 tumors, underwent high-resolution MRI scans with varying voxel sizes on different MRI machines at Yale New Haven Hospital. Tumors were segmented using both manual and AI-based methods, and the effect of voxel size on segmentation precision was quantified through volume measurements, Dice similarity coefficients, and Hausdorff distances.ResultsResults indicate that larger voxel sizes (1.2 × 0.9 × 4.0 mm) significantly reduced segmentation accuracy when compared to smaller voxel sizes (0.5 × 0.5 × 0.8 mm). In addition, AI-based segmentation outperformed manual methods, particularly at larger voxel sizes.DiscussionThese findings highlight the importance of optimizing voxel resolution for accurate tumor monitoring and suggest that AI-driven segmentation may improve consistency and precision in NF2-SWN tumor surveillance. |
| format | Article |
| id | doaj-art-3a9c9886e5a844cdbfbd696f0bc30f33 |
| institution | Kabale University |
| issn | 2673-8740 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Radiology |
| spelling | doaj-art-3a9c9886e5a844cdbfbd696f0bc30f332025-08-20T03:59:22ZengFrontiers Media S.A.Frontiers in Radiology2673-87402025-08-01510.3389/fradi.2025.16182611618261Evaluating the effect of voxel size on the accuracy of 3D volumetric analysis measurements of brain tumorsRithvik S. Ghankot0Manwi Singh1Shelby T. Desroches2Noemi Jester3Amit Mahajan4Samantha Lorr5Frank D. Buono6Daniel H. Wiznia7Daniel H. Wiznia8Michele H. Johnson9Steven M. Tommasini10Steven M. Tommasini11School of Engineering and Applied Science, Yale University, New Haven, CT, United StatesSchool of Medicine, University of Sheffield, Sheffield, United KingdomSchool of Engineering and Applied Science, Yale University, New Haven, CT, United StatesSchool of Medicine, University of Sheffield, Sheffield, United KingdomDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United StatesSchool of Engineering and Applied Science, Yale University, New Haven, CT, United StatesDepartment of Psychiatry, Yale School of Medicine, New Haven, CT, United StatesSchool of Engineering and Applied Science, Yale University, New Haven, CT, United StatesDepartment of Orthopedics, Yale School of Medicine, New Haven, CT, United StatesDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United StatesSchool of Engineering and Applied Science, Yale University, New Haven, CT, United StatesDepartment of Orthopedics, Yale School of Medicine, New Haven, CT, United StatesIntroductionNeurofibromatosis type 2 related Schwannomatosis (NF2-SWN) is a genetic disorder characterized by the growth of vestibular schwannomas (VS), which often leads to progressive hearing loss and vestibular dysfunction. Accurate volumetric assessment of VS tumors is crucial for effective monitoring and treatment planning. Since tumor growth dynamics are often subtle, the resolution of MRI scans plays a critical role in detecting small volumetric changes that inform clinical decisions. This study evaluates the impact of MRI voxel resolution on the accuracy of manual and AI-driven volumetric segmentation of VS in NF2-SWN patients.MethodsTen patients with NF2-SWN, totaling 17 tumors, underwent high-resolution MRI scans with varying voxel sizes on different MRI machines at Yale New Haven Hospital. Tumors were segmented using both manual and AI-based methods, and the effect of voxel size on segmentation precision was quantified through volume measurements, Dice similarity coefficients, and Hausdorff distances.ResultsResults indicate that larger voxel sizes (1.2 × 0.9 × 4.0 mm) significantly reduced segmentation accuracy when compared to smaller voxel sizes (0.5 × 0.5 × 0.8 mm). In addition, AI-based segmentation outperformed manual methods, particularly at larger voxel sizes.DiscussionThese findings highlight the importance of optimizing voxel resolution for accurate tumor monitoring and suggest that AI-driven segmentation may improve consistency and precision in NF2-SWN tumor surveillance.https://www.frontiersin.org/articles/10.3389/fradi.2025.1618261/fullneurofibromatosis type 2 related schwannomatosisvoxel sizedice scoreHausdorff distanceAIsegmentation |
| spellingShingle | Rithvik S. Ghankot Manwi Singh Shelby T. Desroches Noemi Jester Amit Mahajan Samantha Lorr Frank D. Buono Daniel H. Wiznia Daniel H. Wiznia Michele H. Johnson Steven M. Tommasini Steven M. Tommasini Evaluating the effect of voxel size on the accuracy of 3D volumetric analysis measurements of brain tumors Frontiers in Radiology neurofibromatosis type 2 related schwannomatosis voxel size dice score Hausdorff distance AI segmentation |
| title | Evaluating the effect of voxel size on the accuracy of 3D volumetric analysis measurements of brain tumors |
| title_full | Evaluating the effect of voxel size on the accuracy of 3D volumetric analysis measurements of brain tumors |
| title_fullStr | Evaluating the effect of voxel size on the accuracy of 3D volumetric analysis measurements of brain tumors |
| title_full_unstemmed | Evaluating the effect of voxel size on the accuracy of 3D volumetric analysis measurements of brain tumors |
| title_short | Evaluating the effect of voxel size on the accuracy of 3D volumetric analysis measurements of brain tumors |
| title_sort | evaluating the effect of voxel size on the accuracy of 3d volumetric analysis measurements of brain tumors |
| topic | neurofibromatosis type 2 related schwannomatosis voxel size dice score Hausdorff distance AI segmentation |
| url | https://www.frontiersin.org/articles/10.3389/fradi.2025.1618261/full |
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