Robust Glioblastoma Segmentation and Volumetry Without T2-FLAIR: External Validation of Targeted Dropout Training
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
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2026
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| _version_ | 1866914478355906560 |
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| author | Öchsner, Marco Kaiser, Lena Stahl, Robert Albert, Nathalie L. Liebig, Thomas Forbrig, Robert Reis, Jonas |
| author_facet | Öchsner, Marco Kaiser, Lena Stahl, Robert Albert, Nathalie L. Liebig, Thomas Forbrig, Robert Reis, Jonas |
| contents | Objectives: To externally validate targeted T2 fluid-attenuated inversion recovery (T2-FLAIR) dropout for robust automated glioblastoma segmentation and whole-tumor volumetry without T2-FLAIR, while preserving performance when the full MRI protocol is available. Methods: In this retrospective multi-dataset study, 3D nnU-Net models were developed on BraTS 2021 (n=848) and externally validated on an independent University of Pennsylvania glioblastoma cohort (n=403). Models were trained with or without targeted T2-FLAIR dropout, zeroing the T2-FLAIR channel during training. Testing used prespecified T2-FLAIR-present and T2-FLAIR-absent scenarios; the absent scenario was simulated by zeroing the T2-FLAIR channel at inference. The primary endpoint was per-patient overall region-wise Dice similarity coefficient (DSC). Secondary endpoints were region-specific DSC, 95th percentile Hausdorff distance, and Bland-Altman whole-tumor volume bias. Results: In external validation, performance was preserved with the full MRI protocol: overall median DSC was 94.8% (interquartile range [IQR] 90.0%-97.1%) with dropout and 95.0% (IQR 90.3%-97.1%) without dropout. In the T2-FLAIR-absent scenario, targeted dropout improved overall median DSC from 81.0% (IQR 75.1%-86.4%) to 93.4% (IQR 89.1%-96.2%). Whole-tumor DSC improved from 60.4% to 92.6%, whole-tumor 95th percentile Hausdorff distance from 17.24 mm to 2.45 mm, and whole-tumor volume bias from -45.6 mL to 0.83 mL. Conclusions: In an independent external test cohort, targeted T2-FLAIR dropout preserved glioblastoma segmentation performance with the full MRI protocol and substantially reduced whole-tumor segmentation error and volumetric bias when T2-FLAIR was absent. These findings support targeted sequence dropout as a practical robustness strategy for automated glioblastoma analysis in retrospective and heterogeneous clinical workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_20218 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Robust Glioblastoma Segmentation and Volumetry Without T2-FLAIR: External Validation of Targeted Dropout Training Öchsner, Marco Kaiser, Lena Stahl, Robert Albert, Nathalie L. Liebig, Thomas Forbrig, Robert Reis, Jonas Image and Video Processing Quantitative Methods Objectives: To externally validate targeted T2 fluid-attenuated inversion recovery (T2-FLAIR) dropout for robust automated glioblastoma segmentation and whole-tumor volumetry without T2-FLAIR, while preserving performance when the full MRI protocol is available. Methods: In this retrospective multi-dataset study, 3D nnU-Net models were developed on BraTS 2021 (n=848) and externally validated on an independent University of Pennsylvania glioblastoma cohort (n=403). Models were trained with or without targeted T2-FLAIR dropout, zeroing the T2-FLAIR channel during training. Testing used prespecified T2-FLAIR-present and T2-FLAIR-absent scenarios; the absent scenario was simulated by zeroing the T2-FLAIR channel at inference. The primary endpoint was per-patient overall region-wise Dice similarity coefficient (DSC). Secondary endpoints were region-specific DSC, 95th percentile Hausdorff distance, and Bland-Altman whole-tumor volume bias. Results: In external validation, performance was preserved with the full MRI protocol: overall median DSC was 94.8% (interquartile range [IQR] 90.0%-97.1%) with dropout and 95.0% (IQR 90.3%-97.1%) without dropout. In the T2-FLAIR-absent scenario, targeted dropout improved overall median DSC from 81.0% (IQR 75.1%-86.4%) to 93.4% (IQR 89.1%-96.2%). Whole-tumor DSC improved from 60.4% to 92.6%, whole-tumor 95th percentile Hausdorff distance from 17.24 mm to 2.45 mm, and whole-tumor volume bias from -45.6 mL to 0.83 mL. Conclusions: In an independent external test cohort, targeted T2-FLAIR dropout preserved glioblastoma segmentation performance with the full MRI protocol and substantially reduced whole-tumor segmentation error and volumetric bias when T2-FLAIR was absent. These findings support targeted sequence dropout as a practical robustness strategy for automated glioblastoma analysis in retrospective and heterogeneous clinical workflows. |
| title | Robust Glioblastoma Segmentation and Volumetry Without T2-FLAIR: External Validation of Targeted Dropout Training |
| topic | Image and Video Processing Quantitative Methods |
| url | https://arxiv.org/abs/2602.20218 |