3 March 2017 Classification of clinical significance of MRI prostate findings using 3D convolutional neural networks
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Abstract
Prostate cancer (PCa) remains a leading cause of cancer mortality among American men. Multi-parametric magnetic resonance imaging (mpMRI) is widely used to assist with detection of PCa and characterization of its aggressiveness. Computer-aided diagnosis (CADx) of PCa in MRI can be used as clinical decision support system to aid radiologists in interpretation and reporting of mpMRI. We report on the development of a convolution neural network (CNN) model to support CADx in PCa based on the appearance of prostate tissue in mpMRI, conducted as part of the SPIE-AAPM-NCI PROSTATEx challenge. The performance of different combinations of mpMRI inputs to CNN was assessed and the best result was achieved using DWI and DCE-MRI modalities together with the zonal information of the finding. On the test set, the model achieved an area under the receiver operating characteristic curve of 0.80.
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Alireza Mehrtash, Alireza Sedghi, Mohsen Ghafoorian, Mehdi Taghipour, Clare M. Tempany, William M. Wells, Tina Kapur, Parvin Mousavi, Purang Abolmaesumi, Andriy Fedorov, "Classification of clinical significance of MRI prostate findings using 3D convolutional neural networks", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101342A (3 March 2017); doi: 10.1117/12.2277123; https://doi.org/10.1117/12.2277123
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