NIMG-88. A TRANSFER LEARNING APPROACH FOR AUTOMATIC SEGMENTATION OF TUMOR SUB-COMPARTMENTS IN PEDIATRIC MEDULLOBLASTOMA USING MULTIPARAMETRIC MRI: PRELIMINARY FINDINGS. (14th November 2022) - Bareja, Rohan; Ismail, Marwa; Martin, Douglas; Nayate, Ameya; Tamrazi, Benita; Salloum, Ralph; Margol, Ashley; Judkins, Alexander; Iyer, Sukanya; de Blank, Peter; Tiwari, Pallavi
Abstract: 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.