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

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.

[1]  D. Margolis,et al.  PI-RADS Prostate Imaging - Reporting and Data System: 2015, Version 2. , 2016, European urology.

[2]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[3]  Nico Karssemeijer,et al.  Computer-Aided Detection of Prostate Cancer in MRI , 2014, IEEE Transactions on Medical Imaging.

[4]  Pablo Lamata,et al.  Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation , 2016, RAMBO+HVSMR@MICCAI.

[5]  H. Huisman,et al.  Prostate cancer: computer-aided diagnosis with multiparametric 3-T MR imaging--effect on observer performance. , 2013, Radiology.

[6]  Simone Schrading,et al.  Abbreviated Biparametric Prostate MR Imaging in Men with Elevated Prostate-specific Antigen. , 2017, Radiology.

[7]  Farhad Pishgar,et al.  Global, Regional and National Burden of Prostate Cancer, 1990 to 2015: Results from the Global Burden of Disease Study 2015 , 2017, The Journal of urology.

[8]  Xin Yang,et al.  Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks , 2017, Physics in medicine and biology.

[9]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[10]  Mehdi Moradi,et al.  A data-driven approach to prostate cancer detection from dynamic contrast enhanced MRI , 2015, Comput. Medical Imaging Graph..

[11]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[12]  Martin Jägersand,et al.  Convolutional gated recurrent networks for video segmentation , 2016, 2017 IEEE International Conference on Image Processing (ICIP).

[13]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[15]  John Trachtenberg,et al.  The burden of prostate cancer in Canada. , 2013, Canadian Urological Association journal = Journal de l'Association des urologues du Canada.

[16]  Ron Kikinis,et al.  Statistical validation of image segmentation quality based on a spatial overlap index. , 2004, Academic radiology.

[17]  Wei Li,et al.  Prostate cancer diagnosis using deep learning with 3D multiparametric MRI , 2017, Medical Imaging.

[18]  Christopher Joseph Pal,et al.  Delving Deeper into Convolutional Networks for Learning Video Representations , 2015, ICLR.

[19]  Jin Hyung Lee,et al.  DWI of the spinal cord with reduced FOV single‐shot EPI , 2008, Magnetic resonance in medicine.

[20]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[21]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[22]  P. Choyke,et al.  Validation of the Dominant Sequence Paradigm and Role of Dynamic Contrast-enhanced Imaging in PI-RADS Version 2. , 2017, Radiology.

[23]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[24]  Tao Liu,et al.  Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning , 2017, Scientific Reports.

[25]  Carlos Nicolau,et al.  Detection of Clinically Significant Prostate Cancer: Short Dual-Pulse Sequence versus Standard Multiparametric MR Imaging-A Multireader Study. , 2017, Radiology.

[26]  B. Das,et al.  Basic Principles of MR Imaging , 2015 .

[27]  J P B O'Connor,et al.  Dynamic contrast-enhanced imaging techniques: CT and MRI. , 2011, The British journal of radiology.