Recent studies in medical image segmentation involve new automatic approaches where active learning models are useful with less training samples. Presence of homogenous and heterogenous intensities for a single anatomical structure in pediatric musculoskeletal MR images affects the accuracy in terms of segmentation and classification of labels. This study addresses the homogeneity in intensity issues and introduces a new pre-training pipeline framework of Multi-level Otsu thresholding image as separate channel for 3D UNet model training. The proposed framework achieved higher performance of up to 85% when compared with the Baseline 3D UNet model and the Histogram threshold with 3D UNet. All algorithms are run through MONAI core framework.Clinical Relevance- This study will be of major interest to practicing pediatric clinicians and surgeons for its ability to provide accurate morphological assessment of underlying musculoskeletal structure. For researchers, it provides a new approach in dealing with heterogeneity in intensity problem which is common in pediatric MR imaging.