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Comput Med Imaging Graph. 2019 Jul;75:14-23. doi: 10.1016/j.compmedimag.2019.04.006. Epub 2019 May 19.

Model-free prostate cancer segmentation from dynamic contrast-enhanced MRI with recurrent convolutional networks: A feasibility study.

Author information

1
Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada.
2
Biomedical Translational Imaging Centre, Nova Scotia Health Authority and IWK Health Centre, Halifax, NS, Canada.
3
Nova Scotia Health Research Foundation, Halifax, NS, Canada.
4
Biomedical Translational Imaging Centre, Nova Scotia Health Authority and IWK Health Centre, Halifax, NS, Canada; Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada.
5
Department of Pathology, Dalhousie University, Halifax, NS, Canada.
6
Biomedical Translational Imaging Centre, Nova Scotia Health Authority and IWK Health Centre, Halifax, NS, Canada; Department of Physics & Atmospheric Science, Dalhousie University, Halifax, NS, Canada; Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada. Electronic address: sharon.clarke@dal.ca.

Abstract

Dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is a method of temporal imaging that is commonly used to aid in prostate cancer (PCa) diagnosis and staging. Typically, machine learning models designed for the segmentation and detection of PCa will use an engineered scalar image called Ktrans to summarize the information in the DCE time-series images. This work proposes a new model that amalgamates the U-net and the convGRU neural network architectures for the purpose of interpreting DCE time-series in a temporal and spatial basis for segmenting PCa in MR images. Ultimately, experiments show that the proposed model using the DCE time-series images can outperform a baseline U-net segmentation model using Ktrans. However, when other types of scalar MR images are considered by the models, no significant advantage is observed for the proposed model.

KEYWORDS:

Dynamic contrast enhancement; Magnetic resonance imaging; Prostate cancer; Recurrent convolutional networks

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