Abstract
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.
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Acknowledgments
Preclinical MRI data were acquired by Xia Ge and John Engelbach. Funding was provided by NCI grant U24 CA209837, Washington University Co-Clinical Imaging Research Resource, and the Small-Animal Cancer Imaging Shared Resource of the Alvin J. Siteman Cancer Center, an NCI-Designated Comprehensive Cancer Center (Cancer Center Support Grant P30 CA91842).
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Roy, S., Shoghi, K.I. (2019). Computer-Aided Tumor Segmentation from T2-Weighted MR Images of Patient-Derived Tumor Xenografts. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11663. Springer, Cham. https://doi.org/10.1007/978-3-030-27272-2_14
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DOI: https://doi.org/10.1007/978-3-030-27272-2_14
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