Journal
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
Volume 75, Issue -, Pages 14-23Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compmedimag.2019.04.006
Keywords
Magnetic resonance imaging; Dynamic contrast enhancement; Prostate cancer; Recurrent convolutional networks
Funding
- Canada Summer Jobs
- Atlantic Innovation Fund award
- Brain Canada
- NSERC Discovery program
- GE Healthcare Investigator Initiated Research grant
- Radiology Research Foundation
- Nova Scotia Health Authority Research Fund
- Nova Scotia Cooperative Education Incentive
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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 K-trans 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 K-trans. However, when other types of scalar MR images are considered by the models, no significant advantage is observed for the proposed model. (C) 2019 Elsevier Ltd. All rights reserved.
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