Magnetic resonance imaging (MRI) is typically used to detect and assess therapeutic response in preclinical imaging of patient-derived tumor xenografts (PDX). The overarching objective of the work is to develop an automated methodology to detect and segment tumors in PDX for subsequent analyses. Automated segmentation also has the benefit that it will minimize user bias. A hybrid method combining fast k-means, morphology, and level set is used to localize and segment tumor volume from volumetric MR images. Initial centroids of k-means are selected by local density peak estimation method. A new variational model is implemented to exploit the region information by minimizing energy functional in level set. The mask specific initialization approach is used to create a genuine boundary of level set. Performance of tumor segmentation is compared with manually segmented image and to established algorithms. Segmentation results obtained from six metrics are Jaccard score (>80%), Dice score (>85%), F score (>85%), G-mean (>90%), volume similarity matrix (>95%) and relative volume error (<8%). The proposed method reliably localizes and segments PDX tumors and has the potential to facilitate high-throughput analysis of MR imaging in co-clinical trials involving PDX.