NIMG-88. A TRANSFER LEARNING APPROACH FOR AUTOMATIC SEGMENTATION OF TUMOR SUB-COMPARTMENTS IN PEDIATRIC MEDULLOBLASTOMA USING MULTIPARAMETRIC MRI: PRELIMINARY FINDINGS
| Autor: | Rohan Bareja, Marwa Ismail, Douglas Martin, Ameya Nayate, Benita Tamrazi, Ralph Salloum, Ashley Margol, Alexander Judkins, Sukanya Iyer, Peter de Blank, Pallavi Tiwari |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Neuro-Oncology. 24:vii185-vii186 |
| ISSN: | 1523-5866 1522-8517 |
| Popis: | PURPOSE Superior outcomes for medulloblastoma (MB) requires precise surgical resection which can be guided by tumor segmentation. We present the first attempt at automatic segmentation of MB tumors via a hierarchical transfer-learning model that (1) segments the entire tumor habitat (enhancing tumor (ET), necrosis/non-enhancing tumor (NET), edema), followed by (2) training separate models for each of the sub-compartments. Transfer learning from adult brain tumors is used to optimize segmentation of tumor sub-compartments for pediatric MB. METHODS We evaluated 300 adult glioma studies (BRATS) and 49 pediatric MB studies (2-18 years old), both consisting of Gd-T1w, T2w, FLAIR sequences. The MB cohort was collected from Children's Hospital of Los Angeles (Nf19) and Cincinnati Children’s Hospital Medical Center (Nf30). Scans were registered to age-specific pediatric atlases, followed by bias correction and skull-stripping. Ground truth for the tumor sub-compartments was generated via consensus across two experts. We employed a 3D nn-Unet segmentation model on BRATS dataset using initial learning rate of 0.01, stochastic gradient descent as optimizer, and an average of dice loss and cross-entropy loss as the loss function. A hierarchical transfer learning model with Models Genesis was then applied, which allowed for fine tuning every layer on the pediatric MB dataset, across 5-fold cross validation. Dice score was used as performance metric, such that a perfect overlap between ground truth and prediction would yield a Dice score of 1. RESULTS Our 3D hierarchical segmentation model yielded mean dice scores of 0.85±0.03 for the entire tumor habitat; 0.77±0.048 for ET, 0.73±0.09 for edema, and 0.56±0.09 for NET + necrosis segmentation, across cross-validation runs. Overall, tumor outline and segmentation matched well with the ground truth, especially for the entire tumor, ET and enhancing tumor sub-compartments. CONCLUSIONS Our segmentation approach holds promise for accurate automated delineation of the tumor sub-compartments in pediatric Medulloblastoma. |
| Databáze: | OpenAIRE |
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