A Non-linear, Tissue-specific 3D Data Generative Framework for Deep Learning Segmentation Performance Enhancement

Deep learning (DL) models have been successful in solving segmentation problems that involve large, balanced, and labeled datasets. However, in the medical imaging domain, it is rare to find manually annotated datasets that capture the entire spectrum of heterogeneity. Novel datasets with significantly different intensities than training datasets, may adversely affect DL model performance. In this work, we present a hybrid framework for tissue-specific, non-linear intensity transformation of pediatric T2w images similar to that of the adults training dataset and demonstrate an improved performance for vertebra segmentation of pediatric datasets without the need for DL network re-training/re-tuning.