Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging
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We propose an approach of 3D convolutional neural network to segment the prostate in MR images.
A 3D deep dense multi-path convolutional neural network that follows the framework of the encoder–decoder design is proposed. The encoder is built based upon densely connected layers that learn the high-level feature representation of the prostate. The decoder interprets the features and predicts the whole prostate volume by utilizing a residual layout and grouped convolution. A set of sub-volumes of MR images, centered at the prostate, is generated and fed into the proposed network for training purpose. The performance of the proposed network is compared to previously reported approaches.
Two independent datasets were employed to assess the proposed network. In quantitative evaluations, the proposed network achieved 95.11 and 89.01 Dice coefficients for the two datasets. The segmentation results were robust to variations in MR images. In comparison experiments, the segmentation performance of the proposed network was comparable to the previously reported approaches. In qualitative evaluations, the segmentation results by the proposed network were well matched to the ground truth provided by human experts.
The proposed network is capable of segmenting the prostate in an accurate and robust manner. This approach can be applied to other types of medical images.
KeywordsDeep learning Prostate segmentation Magnetic resonance imaging Dense connections Grouped convolution
This was supported by the National Research Foundation of Korea through the Korea Government (MSIP) under Grant 2016R1C1B2012433.
Compliance with ethical standards
Conflict of interest
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 Declaration of Helsinki and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
- 2.Fütterer JJ, Briganti A, De Visschere P, Emberton M, Giannarini G, Kirkham A, Taneja SS, Thoeny H, Villeirs G, Villers A (2015) Can clinically significant prostate cancer be detected with multiparametric magnetic resonance imaging? a systematic review of the literature. Eur Urol 68(6):1045–1053CrossRefPubMedGoogle Scholar
- 6.Ghai S, Louis AS, Van Vliet M, Lindner U, Haider MA, Hlasny E, Spensieri P, Van Der Kwast TH, McCluskey SA, Kucharczyk W (2015) Real-time MRI-guided focused ultrasound for focal therapy of locally confined low-risk prostate cancer: feasibility and preliminary outcomes. Am J Roentgenol 205(2):W177–W184CrossRefGoogle Scholar
- 7.Zhu Y, Williams S, Zwiggelaar R (2004) Segmentation of volumetric prostate MRI data using hybrid 2D + 3D shape modeling. In: Proceeding of medical image understanding and analysis, pp 61–64Google Scholar
- 8.Allen PD, Graham J, Williamson DC, Hutchinson CE (2006) Differential segmentation of the prostate in MR images using combined 3D shape modelling and voxel classification. In: 3rd IEEE international symposium on biomedical imaging: nano to macro. IEEE, pp 410–413Google Scholar
- 11.Vikal S, Haker S, Tempany C, Fichtinger G (2009) Prostate contouring in MRI guided biopsy. In: Medical imaging 2009: image processing. International society for optics and photonics, p 72594AGoogle Scholar
- 15.Klein S, van der Heide UA, Raaymakers BW, Kotte AN, Staring M, Pluim JP (2007) Segmentation of the prostate in MR images by atlas matching. In: 4th IEEE international symposium on biomedical imaging: from nano to macro, 2007. ISBI 2007. IEEE, pp 1300–1303Google Scholar
- 16.Flores-Tapia D, Thomas G, Venugopal N, McCurdy B, Pistorius S (2008) Semi automatic MRI prostate segmentation based on wavelet multiscale products. In: Engineering in medicine and biology society, 2008. EMBS 2008. 30th annual international conference of the IEEE. IEEE, pp 3020–3023Google Scholar
- 17.Fotin SV, Yin Y, Periaswamy S, Kunz J, Haldankar H, Muradyan N, Cornud F, Turkbey B, Choyke PL (2012) Normalized gradient fields cross-correlation for automated detection of prostate in magnetic resonance images. In: Proceedings of the SPIE, vol 8314. https://doi.org/10.1117/12.911620
- 18.Yin Y, Fotin SV, Periaswamy S, Kunz J, Haldankar H, Muradyan N, Cornud F, Turkbey B, Choyke P (2012) Fully automated prostate segmentation in 3D MR based on normalized gradient fields cross-correlation initialization and LOGISMOS refinement. In: Medical imaging 2012: image processing. International Society for Optics and Photonics, p 831406Google Scholar
- 20.Roth HR, Lu L, Farag A, Shin H-C, Liu J, Turkbey EB, Summers RM (2015) Deeporgan: multi-level deep convolutional networks for automated pancreas segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 556–564Google Scholar
- 21.Chen H, Qi X, Yu L, Heng P-A (2016) Dcan: deep contour-aware networks for accurate gland segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2487–2496Google Scholar
- 23.Cheng R, Roth HR, Lu L, Wang S, Turkbey B, Gandler W, McCreedy ES, Agarwal HK, Choyke P, Summers RM (2016) Active appearance model and deep learning for more accurate prostate segmentation on MRI. In: Medical imaging 2016: image processing. International Society for Optics and Photonics, p 97842IGoogle Scholar
- 24.Zhu Q, Du B, Turkbey B, Choyke PL, Yan P (2017) Deeply-supervised CNN for prostate segmentation. In: 2017 international joint conference on neural networks (IJCNN). IEEE, pp 178–184Google Scholar
- 25.Yu L, Yang X, Chen H, Qin J, Heng P-A (2017) Volumetric ConvNets with mixed residual connections for automated prostate segmentation from 3D MR images. In: AAAI, pp 66–72Google Scholar
- 26.Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 234–241Google Scholar
- 27.Noh H, Hong S, Han B (2015) Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE international conference on computer vision, pp 1520–1528Google Scholar
- 28.Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826Google Scholar
- 29.He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778Google Scholar
- 30.Huang G, Liu Z, Weinberger KQ, van der Maaten L (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, vol 2, p 3Google Scholar
- 31.Litjens G, Toth R, van de Ven W, Hoeks C, Kerkstra S, van Ginneken B, Vincent G, Guillard G, Birbeck N, Zhang J, Strand R, Malmberg F, Ou Y, Davatzikos C, Kirschner M, Jung F, Yuan J, Qiu W, Gao Q, Edwards PE, Maan B, van der Heijden F, Ghose S, Mitra J, Dowling J, Barratt D, Huisman H, Madabhushi A (2014) Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge. Med Image Anal 18(2):359–373. https://doi.org/10.1016/j.media.2013.12.002 CrossRefPubMedGoogle Scholar
- 32.Xie S, Girshick R, Dollár P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 5987–5995Google Scholar
- 33.Kinga D, Adam JB (2015) A method for stochastic optimization. In: International conference on learning representations (ICLR)Google Scholar
- 34.Milletari F, Navab N, Ahmadi S-A (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 4th international conference on 3D vision (3DV). IEEE, pp 565–571Google Scholar
- 35.Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D U-net: learning dense volumetric segmentation from sparse annotation. In: Medical image computing and computer-assisted intervention—MICCAI 2016. Springer International Publishing, Cham, pp 424–432Google Scholar
- 36.Tsehay YK, Lay NS, Roth HR, Wang X, Kwak JT, Turkbey BI, Pinto PA, Wood BJ, Summers RM (2017) Convolutional neural network based deep-learning architecture for prostate cancer detection on multiparametric magnetic resonance images. In: SPIE medical imaging. SPIE, p 11Google Scholar