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.

Original languageEnglish
Title of host publicationImage Analysis and Recognition - 16th International Conference, ICIAR 2019, Proceedings
EditorsFakhri Karray, Alfred Yu, Aurélio Campilho
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783030272715
StatePublished - 2019
Event16th International Conference on Image Analysis and Recognition, ICIAR 2019 - Waterloo, Canada
Duration: Aug 27 2019Aug 29 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11663 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Conference on Image Analysis and Recognition, ICIAR 2019


  • Computer aided detection
  • Level set
  • Magnetic resonance imaging
  • Tumor segmentation


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