A Deep Learning-Based Approach for the Detection and Localization of Prostate Cancer in T2 Magnetic Resonance Images
- 413 Downloads
We address the problem of prostate lesion detection, localization, and segmentation in T2W magnetic resonance (MR) images. We train a deep convolutional encoder-decoder architecture to simultaneously segment the prostate, its anatomical structure, and the malignant lesions. To incorporate the 3D contextual spatial information provided by the MRI series, we propose a novel 3D sliding window approach, which preserves the 2D domain complexity while exploiting 3D information. Experiments on data from 19 patients provided for the public by the Initiative for Collaborative Computer Vision Benchmarking (I2CVB) show that our approach outperforms traditional pattern recognition and machine learning approaches by a significant margin. Particularly, for the task of cancer detection and localization, the system achieves an average AUC of 0.995, an accuracy of 0.894, and a recall of 0.928. The proposed mono-modal deep learning-based system performs comparably to other multi-modal MR-based systems. It could improve the performance of a radiologist in prostate cancer diagnosis and treatment planning.
KeywordsMagnetic resonance imaging Prostate cancer Deep convolutional encoder-decoder
The authors would also like to thank Dr. Waleed Hassen and Dr. Eric Vens from Cleveland Clinic, Abu Dhabi, and Dr. Salah El-Rai from Sheikh Khalifa General Hospital for their support and collaboration.
This work is support by a research grant from Al-Jalila foundation ref. AJF-201616.
Compliance with Ethical Standards
Conflict of interests
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
- 1.Alkadi R, Taher F, El-Baz A, Naoufel W: Early diagnosis and staging of prostate cancer using magnetic resonance imaging: State of the art and perspectives. In: Prostate cancer imaging: An engineering and clinical perspective, chapter 2. Taylor & Francis, In-pressGoogle Scholar
- 5.Csurka G, Larlus D, Perronnin F, Meylan F: What is a good evaluation measure for semantic segmentation?. In: BMVC, volume 27, p 2013. Citeseer, 2013Google Scholar
- 7.Eigen D, Fergus R: Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture.. In: Proceedings of the IEEE international conference on computer vision, 2015, pp 2650–2658Google Scholar
- 10.Garcia-Garcia A, Orts-Escolano S, Oprea S, Villena-Martinez V, Garcia-Rodriguez J: A review on deep learning techniques applied to semantic segmentation, 2017. arXiv:1704.06857
- 13.Hall MA: Correlation-based feature selection of discrete and numeric class machine learning, 2000Google Scholar
- 14.Han H, Wang W-Y, Mao B-H: Borderline-smote: a new over- sampling method in imbalanced data sets learning.. In: International Conference on Intelligent Computing, pp 878–887. Springer , 2005Google Scholar
- 15.He K, Zhang X, Ren S, Sun J: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.. In: Proceedings of the IEEE international conference on computer vision, pp 1026–1034, 2015Google Scholar
- 16.Kiraly AP, Nader CA, Tuysuzoglu A, Grimm R, Kiefer B, El-Zehiry N, Kamen A: Deep convolutional encoder-decoders for prostate cancer detection and classification.. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp 489–497. Springer, 2017Google Scholar
- 18.Kumar D, Wong A, Clausi DA: Lung nodule classification using deep features in ct images.. In: 2015 12th conference on computer and robot vision (CRV), pp 133–138. IEEE, 2015Google Scholar
- 19.Lemaitre G: Computer-aided diagnosis for prostate cancer using multi-parametric magnetic resonance imaging. PhD thesis, Ph. D. dissertation, Universitat de Girona and Université de Bourgogne, 2016Google Scholar
- 21.Lemaitre G, Martí R, Rastgoo M, Mériaudeau F: Computer-aided detection for prostate cancer detection based on multi-parametric magnetic resonance imaging.. In: Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE, pp 3138–3141. IEEE, 2017Google Scholar
- 24.Long J, Shelhamer E, Darrell T: Fully convolutional networks for semantic segmentation.. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp 3431–3440Google Scholar
- 26.Mani I, Zhang I: knn approach to unbalanced data distributions: a case study involving information extraction.. In: Proceedings of workshop on learning from imbalanced datasets, vol 126, 2003Google Scholar
- 27.Mazurowski MA, Buda M, Saha A, Bashir MR: Deep learning in radiology: an overview of the concepts and a survey of the state of the art, 2018. arXiv:1802.08717
- 29.Qi CR, Hao SU, Nießner M, Dai A, Yan M, Guibas LJ: Volumetric and multi-view cnns for object classification on 3d data.. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp 5648–5656Google Scholar
- 31.Reda I, Shalaby A, Khalifa F, Elmogy M, Aboulfotouh A, El-Ghar MA, Hosseini-Asl E, Werghi N, Keynton R, El-Baz A: Computer-aided diagnostic tool for early detection of prostate cancer.. In: IEEE international conference on image processing (ICIP), pp 2668–2672. IEEE, 2016Google Scholar
- 32.Ronneberger O, Fischer P, Brox T: U-net: Convolutional networks for biomedical image segmentation.. In: International Conference on Medical image computing and computer-assisted intervention, pp 234–241. Springer, 2015Google Scholar
- 34.Roth HR, Lu L, Seff A, Cherry KM, Hoffman J, Wang S, Liu J, Turkbey E, Summers RM: A new 2.5 d representation for lymph node detection using random sets of deep convolutional neural network observations.. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp 520–527. Springer, 2014Google Scholar
- 36.Simonyan K, Zisserman A: Very deep convolutional networks for large-scale image recognition, 2014. arXiv:1409.1556
- 43.Viswanath S, Bloch BN, Genega E, Rofsky N, Lenkinski R, Chappelow J, Toth R, Madabhushi A: A comprehensive segmentation, registration, and cancer detection scheme on 3 tesla in vivo prostate dce-mri.. In: International conference on medical image computing and computer-assisted intervention, pp 662–669. Springer, 2008Google Scholar
- 44.Viswanath SE, Bloch NB, Chappelow JC, Toth R, Rofsky NM, Genega EM, Lenkinski RE, Madabhushi A: Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 tesla endorectal, in vivo t2- weighted mr imagery. J Magn Reson Imaging 36 (1): 213–224, 2012CrossRefGoogle Scholar
- 46.Wang L, Zwiggelaar R: 3d texton based prostate cancer detection using multiparametric magnetic resonance imaging.. In: Annual conference on medical image understanding and analysis, pp 309– 319. Springer, 2017Google Scholar
- 47.Wang Z, Liu C, Cheng D, Wanga L, Yang X, Chengb K-TT: Automated detection of clinically significant prostate cancer in mp-mri images based on an end-to-end deep neural network. IEEE Transactions on Medical Imaging, 2018Google Scholar
- 48.Zhirong WU, Song S, Khosla A, Fisher YU, Zhang L, Tang X, Xiao J: 3d shapenets; A deep representation for volumetric shapes.. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp 1912–1920Google Scholar
- 50.Yang X, Wang Z, Liu C, Le HM, Chen J, Cheng K-TT, Wang L: Joint detection and diagnosis of prostate cancer in multi-parametric mri based on multimodal convolutional neural networks.. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp 426–434. Springer, 2017Google Scholar
- 51.Lequan YU, Yang X, Chen H, Qin J, Heng P-A: Volumetric convnets with mixed residual connections for automated prostate segmentation from 3d mr images.. In: AAAI, 2017, pp 66–72Google Scholar
- 52.Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A: Learning deep features for discriminative localization.. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 2921–2929. IEEE, 2016Google Scholar