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

Autor: Thomas Trappenberg, Steve Patterson, Steven D. Beyea, Chris V. Bowen, Jennifer Merrimen, Alessandro Guida, Peter Q. Lee, Sharon E. Clarke, Cheng Wang
Rok vydání: 2018
Předmět:
Male
Computer science
Physics::Medical Physics
Contrast Media
Health Informatics
030218 nuclear medicine & medical imaging
Machine Learning
03 medical and health sciences
Prostate cancer
0302 clinical medicine
medicine
Image Processing
Computer-Assisted
Humans
Radiology
Nuclear Medicine and imaging
Segmentation
Aged
Radiological and Ultrasound Technology
Artificial neural network
medicine.diagnostic_test
business.industry
Prostate
Prostatic Neoplasms
Magnetic resonance imaging
Pattern recognition
Model free
Middle Aged
medicine.disease
Computer Graphics and Computer-Aided Design
Magnetic Resonance Imaging
Dynamic contrast
Computer Science::Computer Vision and Pattern Recognition
Dynamic contrast-enhanced MRI
Feasibility Studies
Computer Vision and Pattern Recognition
Artificial intelligence
Neural Networks
Computer
Mr images
business
030217 neurology & neurosurgery
Algorithms
Zdroj: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society. 75
ISSN: 1879-0771
Popis: 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.
Databáze: OpenAIRE
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