TY - GEN
T1 - Computer-aided tumor segmentation from t2-weighted mr images of patient-derived tumor xenografts
AU - Roy, Sudipta
AU - Shoghi, Kooresh Isaac
N1 - Funding Information:
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).
Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Computer aided detection
KW - Level set
KW - Magnetic resonance imaging
KW - Tumor segmentation
UR - http://www.scopus.com/inward/record.url?scp=85071453434&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-27272-2_14
DO - 10.1007/978-3-030-27272-2_14
M3 - Conference contribution
AN - SCOPUS:85071453434
SN - 9783030272715
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 159
EP - 171
BT - Image Analysis and Recognition - 16th International Conference, ICIAR 2019, Proceedings
A2 - Karray, Fakhri
A2 - Yu, Alfred
A2 - Campilho, Aurélio
PB - Springer Verlag
T2 - 16th International Conference on Image Analysis and Recognition, ICIAR 2019
Y2 - 27 August 2019 through 29 August 2019
ER -