MDB-44. A TRANSFER LEARNING APPROACH FOR AUTOMATIC SEGMENTATION OF TUMOR SUB-COMPARTMENTS AND TUMOR HABITAT IN PEDIATRIC MEDULLOBLASTOMA

PURPOSE: Accurate delineation of pediatric medulloblastoma (MB) tumors is required for accurate surgical resection and efficient treatment planning. However, manual delineation is time consuming and prone to errors and inter-reader variability. We present the first attempt at automatic segmentation of MB tumors via a transfer learning approach that utilizes adult brain tumor segmentations to optimize segmentation of 1) the pediatric tumor habitat, comprising enhancing tumor (ET), necrosis/non-enhancing tumor (NET), and edema sub-compartments, and 2) the individual tumor sub-compartments. METHODS: Our cohort consisted of 300 adult tumor MRI scans (BRATS) and 78 pediatric MB scans (46 training, 32 testing) with Gd-T1w, T2w, and FLAIR protocols. Training set subjects were collected from Children’s Hospital of Los Angeles (N=18) and Cincinnati Children’s Hospital Medical Center (N=28), whereas test set subjects were collected from Children’s Hospital of Philadelphia. Preprocessing included age-specific atlas registration, skull stripping, and bias correction. Then, using nnUnet framework, we trained 3D- deep learning U-net models on BRATS dataset for the tumor sub-compartments: ET, edema, and NET + necrosis, as well as the tumor habitat. Our initial learning rate was 0.01, with stochastic gradient descent as optimizer, and an average of dice loss and cross-entropy loss as the loss function. We then performed transfer learning using Models Genesis on the pediatric subjects. RESULTS: Our segmentation model yielded mean dice scores of 0.87± .02 for tumor habitat, .83± .04 for ET, .742±.05 for edema, and .54±.11 for NET + necrosis, across fivefold cross-validation runs. For test set, our model yielded mean dice scores of 0.80 for the tumor habitat, 0.67 for ET, 0.54 for edema, and 0.28 for NET + necrosis. CONCLUSION: Our transfer learning model shows promise in accurate automatic delineation of the MB tumor habitat and its individual sub-compartments, towards efficient surgical and treatment planning in MB tumors.