research-article

Segmentation of Prostate Peripheral Zone based on Multi-scale Features Enhancement

Authors:

Shu ZhanAuthors Info & Claims

ISICDM 2019: Proceedings of the Third International Symposium on Image Computing and Digital Medicine

Pages 349 - 353

https://doi.org/10.1145/3364836.3364906

Published: 24 August 2019 Publication History

Abstract

According to research statistics that there are about 70% of Prostate Cancer (PCa) has been occurred in Peripheral Zone (PZ). Automatic segmentation of the prostate peripheral zone on T2 weighted (T2w) images is a necessary clinical applications for prostate cancer diagnosis. However, the low contrast, blur contour, and significantly varies of shape serious challenges to accurate segmentation of peripheral zone. In this paper, we first present a deep learning method to segment the peripheral zone automatically. For extracting and encoding multi-level features about peripheral zone more effectively, multi-scale dilated convolution (MDC) and pooling block (MPB) were embedded in the baseline model U-Net. Then, we integrated a soft attention mechanism to focus on the salient features useful for segmentation of PZ. In addition, the joint adversarial loss could enhance the model performance of recognition. Experimental results show that the proposed method yield satisfactory segmentation. The mean DICE coefficient arrived at about 88% as compared to the ground truth. The quantitative and qualitative evaluation demonstrates that the modified generative adversarial network is more effective.

References

[1]

Chen W, Zheng R, and Baade P D, et al. Cancer statistics in China 2015[J]. CA: a cancer journal for clinicians, 2016, 66(2):115--132.

[2]

Siegel R L, Miller K D, Jemal A. Cancer statistics 2018[J]. CA: A Cancer Journal for Clinicians, 2018, 60(5):277--300.

[3]

Zeng W, Peng J, Wang S, et al. A Comparative Study of CNN-Based Super- Resolution Methods in MRI Reconstruction[C]. IEEE 16th International Symposium on Biomedical Imaging(ISBI). 2019, 1678--1682.

[4]

Qiao Z, Liang D, Tang S, et al. Optimization-Based Image Reconstruction From Fast-Scanned, Noisy Projections in EPR Imaging[J]. IEEE Access, 2019, 7:19590--19601.

[5]

Lyu J, Nakarmi U, Liang D, et al. KerNL: Kernel-Based Nonlinear Approach to Parallel MRI Reconstruction[J]. IEEE transactions on medical imaging, 2018, 38(1): 312--321.

[6]

Wang S, Tan S, Gao Y, Liang D, et al. Learning joint-sparse codes for calibration-free parallel MR imaging[J]. IEEE transactions on medical imaging, 2017, 37(1):251--261.

[7]

Liu D, Liang D, Zhang N, et al. Under-sampling trajectory design for compressed sensing based DCE-MRI[C]. IEEE 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC), 2013, 2624--2627.

[8]

Peng Y, Jiang Y, Yang C, et al. Quantitative Analysis of Multiparametric Prostate MR Images: Differentiation between Prostate Cancer and Normal Tissue and Correlation with Gleason ScoreâĀTA Computer-aided Diagnosis Development Study[J]. Radiology, 2013, 267(3):787--796.

[9]

Turkbey B, Mani H, Shah V, et al. Multiparametric 3T Prostate Magnetic Resonance Imaging to Detect Cancer: Histopathological Correlation Using Prostatectomy Specimens Processed in Customized Magnetic Resonance Imaging Based Molds[J]. The Journal of Urology, 2011, 186(5):1818--1824.

[10]

Liu Y, Li C, Guo S, et al. A novel level set method for segmentation of left and right ventricles from cardiac MR images[C]. 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014, 4719--4722.

[11]

Li C, Huang R, Ding Z, et al. A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI[J]. IEEE transactions on image processing, 2011, 20(7):2007--2016.

[12]

Li C, Xu C, Gui C, et al. Distance regularized level set evolution and its application to image segmentation[J]. IEEE transactions on image processing, 2010, 19(12):3243--3254.

[13]

Li C, Li F, Kao C Y, et al. Image segmentation with simultaneous illumination and reflectance estimation: An energy minimization approach[C]. IEEE 12th international conference on computer vision, 2009, 702--708.

[14]

Li C, Gatenby C, Wang L, et al. A robust parametric method for bias field estimation and segmentation of MR images[C]. IEEE Conference on Computer Vision and Pattern Recognition(CVPR), 2009, 218--223.

[15]

LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553):436.

[16]

Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]. Advances in neural information processing systems. 2012, 1097--1105.

[17]

Szegedy C, LiuW, Jia Y, et al. Going deeper with convolutions[C]. IEEE conference on computer vision and pattern recognition(CVPR), 2015, 1--9.

[18]

He K, and Zhang X, Ren S, et al. Deep residual learning for image recognition[C]. IEEE conference on computer vision and pattern recognition (CVPR), 2016, 770--778.

[19]

Huang G, Liu Z, Van Der Maaten L, et al. Densely Connected Convolutional Networks[ C]. IEEE Conference on Computer Vision and Pattern Recognition(CVPR), 2017, 4700--4708.

[20]

Kohl S, Bonekamp D, Schlemmer H P, et al. Adversarial networks for the detection of aggressive prostate cancer[C]. International Conference Image Analysis and Recognition (MICCAI), 2017.

[21]

Esteva A, Kuprel B, Novoa R A, et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 542(7639):115.

[22]

Myronenko A, Yang D, Buch V, et al. 4D CNN for semantic segmentation of cardiac volumetric sequences[J]. arXiv preprint, 2019, arXiv:1906.07295.

[23]

Zhang F, Luo L, Sun X, et al. Cascaded Generative and Discriminative Learning for Microcalcification Detection in Breast Mammograms[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, 12578--12586.

[24]

Clark T, Wong A, Haider M A, et al. Fully deep convolutional neural networks for segmentation of the prostate gland in diffusion-weighted MR images[C]. International Conference Image Analysis and Recognition (MICCAI), 2017, 97--104.

Digital Library

[25]

Tian Z, Liu L, Zhang Z, et al. PSNet: prostate segmentation on MRI based on a convolutional neural network[J]. Journal of Medical Imaging, 2018, 5(2): 021208.

[26]

Anas E M A, Mousavi P, Abolmaesumi P. A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy[J]. Medical image analysis, 2018, 48:107--116.

[27]

Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]. International Conference on Neural Information Processing Systems, 2014, 2672--2680.

[28]

Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C]. International Conference on Medical image computing and computer-assisted intervention(MICCAI), 2015, 234--241.

[29]

He K, Girshick R, Dollar P. Rethinking imagenet pre-training[J]. arXiv preprint, 2018, arXiv:1811.08883.

[30]

Everingham M, Van Gool L, Williams C K I, et al. The Pascal Visual Object Classes (VOC) Challenge[J]. International Journal of Computer Vision, 2010, 88(2):303--338.

Digital Library

[31]

Lin T Y, Maire M, Belongie S, et al. Microsoft coco: Common objects in context[C]. European conference on computer vision (ECCV), 2014, 740--755.

[32]

Shin H C, Roth H R, Gao M, et al. Deep convolutional neural networks for computer-aided detection:CNN architectures, dataset characteristics and transfer learning[J]. IEEE transactions on medical imaging, 2016, 35(5):1285--1298.

[33]

Tajbakhsh N, Shin J Y, Gurudu S R, et al. Convolutional neural networks for medical image analysis: Full training or fine tuning?[J]. IEEE transactions on medical imaging, 2016, 35(5):1299--1312.

[34]

Raghu M, Zhang C, Kleinberg J, et al. Transfusion:Understanding transfer learning with applications to medical imaging[J]. arXiv preprint, 2019, arXiv:1902.07208.

[35]

Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv preprint, 2015, arXiv:1511.06434.

[36]

Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[ C]. IEEE conference on computer vision and pattern recognition(CVPR), 2015, 3431--3440.

[37]

Chen L C, Papandreou G, Kokkinos I, et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs[C]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2016, 40(4):834--848.

[38]

Chen L C, Papandreou G, Schroff F, et al. Rethinking atrous convolution for semantic image segmentation, arXiv preprint, 2017, arXiv:1706.05587.

[39]

Wu H, Zhang J, Huang K, et al. FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation[C]. arXiv preprint, 2019, arXiv:1903.11816.

[40]

Gu Z, Cheng J, Fu H, et al. CE-Net: Context Encoder Network for 2D Medical Image Segmentation[J]. IEEE transactions on medical imaging, (2019)

[41]

He K, Zhang X, Ren S, et al. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2014, 37(9):1904--1916.

[42]

Zhang H, Dana K, Shi J, et al. Context encoding for semantic segmentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 7151--7160, (2018)

[43]

Wang F, Jiang M, Qian C, et al. Residual attention network for image classification[ C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, 3156--3164.

[44]

Oktay O, Schlemper J, Folgoc L L, et al. Attention u-net: Learning where to look for the pancreas[C]. arXiv preprint, 2018, arXiv:1804.03999.

[45]

Paszke A, Gross S, Chintala S, et al. Automatic differentiation in pytorch[J]. In NIPS Workshop, 2017.

[46]

Litjens G, Debats O, Barentsz J, et al. Computer-aided detection of prostate cancer in MRI[J]. IEEE Transactions on Medical Imaging, 2014, 33:1083--1092.

[47]

Nair V, Hinton G E. Rectified linear units improve restricted boltzmann machines, The 27th international conference on machine learning (ICML), 2010, 807--814.

Digital Library

Cited By

Garcea FSerra ALamberti FMorra L(2023)Data augmentation for medical imaging: A systematic literature reviewComputers in Biology and Medicine10.1016/j.compbiomed.2022.106391152(106391)Online publication date: Jan-2023
https://doi.org/10.1016/j.compbiomed.2022.106391
Khan ZYahya NAlsaih KAl-Hiyali MMeriaudeau F(2021)Recent Automatic Segmentation Algorithms of MRI Prostate Regions: A ReviewIEEE Access10.1109/ACCESS.2021.30908259(97878-97905)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3090825

Index Terms

Segmentation of Prostate Peripheral Zone based on Multi-scale Features Enhancement
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation

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ISICDM 2019: Proceedings of the Third International Symposium on Image Computing and Digital Medicine

August 2019

370 pages

ISBN:9781450372626

DOI:10.1145/3364836

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 24 August 2019

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ISICDM 2019: The Third International Symposium on Image Computing and Digital Medicine

August 24 - 26, 2019

Xi'an, China

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Cited By

Garcea FSerra ALamberti FMorra L(2023)Data augmentation for medical imaging: A systematic literature reviewComputers in Biology and Medicine10.1016/j.compbiomed.2022.106391152(106391)Online publication date: Jan-2023
https://doi.org/10.1016/j.compbiomed.2022.106391
Khan ZYahya NAlsaih KAl-Hiyali MMeriaudeau F(2021)Recent Automatic Segmentation Algorithms of MRI Prostate Regions: A ReviewIEEE Access10.1109/ACCESS.2021.30908259(97878-97905)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3090825

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