Abstract
The segmentation of the prostate gland into two sub-regions, namely, the central gland (CG) and the peripheral zone (PZ) is crucial for the prostate cancer (PCa) diagnosis. The nature and occurrence of cancer occurred in the prostate is substantially different in both zones. Magnetic resonance imaging modality (MRI) is a clinically primary tool for computer-based assessment and remediation of various cancer types such as PCa. In this paper, we evaluated DeeplabV3+ model on T2W MRI scans using the I2CVB dataset, which is designed in an encoder-decoder style for the zonal segmentation of prostate regions. An important feature of DeeplabV3+ is the depth-wise separable convolutions, which allow more information to be extracted from images as it uses filters with different dilation rates. Prior to being fed to the deep neural network, image pre-processing techniques are applied, including image resizing, cropping, and denoising. The DeeplabV3+ model performance is evaluated using the Dice similarity coefficient (DSC) metric and compared with the vanilla U-Net architecture. Results show that the encoder-decoder network having depth-wise separable convolutions performed better prostate segmentation than the network with standard convolution operations with the DSC value of 70.1% in PZ and 81.5% in CG zone.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aldoj, N., Biavati, F., Rutz, M., Michallek, F., Stober, S., Dewey, M.: Automatic prostate and prostate zones segmentation of magnetic resonance images using convolutional neural networks (2019)
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_49
Chi, Y., et al.: A compact method for prostate zonal segmentation on multiparametric MRIs. In: Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 9036, p. 90360N. International Society for Optics and Photonics (2014)
Choi, Y.J., Kim, J.K., Kim, N., Kim, K.W., Choi, E.K., Cho, K.S.: Functional MR imaging of prostate cancer. Radiographics 27(1), 63–75 (2007)
Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)
Clark, T., Zhang, J., Baig, S., Wong, A., Haider, M.A., Khalvati, F.: Fully automated segmentation of prostate whole gland and transition zone in diffusion-weighted MRI using convolutional neural networks. J. Med. Imaging 4(4), 041307 (2017)
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)
Dai, J., et al.: Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 764–773 (2017)
Haffner, J., et al.: Peripheral zone prostate cancers: location and intraprostatic patterns of spread at histopathology. The Prostate 69(3), 276–282 (2009)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Jensen, C., et al.: Prostate zonal segmentation in 1.5 T and 3T T2W MRI using a convolutional neural network. J. Med. Imaging 6(1), 014501 (2019)
Khan, Z., Yahya, N., Alsaih, K., Ali, S.S.A., Meriaudeau, F.: Evaluation of deep neural networks for semantic segmentation of prostate in T2W MRI. Sensors 20(11), 3183 (2020)
Khan, Z., Yahya, N., Alsaih, K., Meriaudeau, F.: Zonal segmentation of prostate T2W-MRI using atrous convolutional neural network. In: 2019 IEEE Student Conference on Research and Development (SCOReD), pp. 95–99. IEEE (2019)
Kingma, D., Adam, B.J.: A method for stochastic optimization. arxiv preprint arxiv: 14126980 (2014). Cited on p. 50
Klein, S., Van Der Heide, U.A., Lips, I.M., Van Vulpen, M., Staring, M., Pluim, J.P.: Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Med. Phys. 35(4), 1407–1417 (2008)
Langerak, T.R., van der Heide, U.A., Kotte, A.N., Viergever, M.A., Van Vulpen, M., Pluim, J.P.: Label fusion in atlas-based segmentation using a selective and iterative method for performance level estimation (SIMPLE). IEEE Trans. Med. Imaging 29(12), 2000–2008 (2010)
Leake, J.L., et al.: Prostate MRI: access to and current practice of prostate MRI in the united states. J. Am. Coll. Radiol. 11(2), 156–160 (2014)
Lemaître, G., Martí, R., Freixenet, J., Vilanova, J.C., Walker, P.M., Meriaudeau, F.: Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: a review. Comput. Biol. Med. 60, 8–31 (2015)
Lemaitre, G., Martí, R., Rastgoo, M., Mériaudeau, F.: Computer-aided detection for prostate cancer detection based on multi-parametric magnetic resonance imaging. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3138–3141. IEEE (2017)
Litjens, G.J.S.: Computerized detection of cancer in multi-parametric prostate MRI. Ph.D. thesis, Radboud University, Nijmegen, Netherlands (2015)
Martin, S., Troccaz, J., Daanen, V.: Automated segmentation of the prostate in 3D MR images using a probabilistic atlas and a spatially constrained deformable model. Med. Phys. 37(4), 1579–1590 (2010)
Muller, B.G., et al.: Prostate cancer: interobserver agreement and accuracy with the revised prostate imaging reporting and data system at multiparametric MR imaging. Radiology 277(3), 741–750 (2015)
Perez, L., Wang, J.: The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621 (2017)
Qiu, W., Yuan, J., Ukwatta, E., Sun, Y., Rajchl, M., Fenster, A.: Dual optimization based prostate zonal segmentation in 3D MR images. Med. Image Anal. 18(4), 660–673 (2014)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Scheenen, T.W., Rosenkrantz, A.B., Haider, M.A., Fütterer, J.J.: Multiparametric magnetic resonance imaging in prostate cancer management: current status and future perspectives. Invest. Radiol. 50(9), 594–600 (2015)
Sekou, T.B., Hidane, M., Olivier, J., Cardot, H.: From patch to image segmentation using fully convolutional networks-application to retinal images. arXiv preprint arXiv:1904.03892 (2019)
Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2019. CA: Cancer J. Clin. 69(1), 7–34 (2019)
Toth, R., Madabhushi, A.: Multifeature landmark-free active appearance models: application to prostate MRI segmentation. IEEE Trans. Med. Imaging 31(8), 1638–1650 (2012)
Villeirs, G.M., De Meerleer, G.O.: Magnetic resonance imaging (MRI) anatomy of the prostate and application of MRI in radiotherapy planning. Eur. J. Radiol. 63(3), 361–368 (2007)
Wang, Z., Liu, C., Cheng, D., Wang, L., Yang, X., Cheng, K.T.: Automated detection of clinically significant prostate cancer in MP-MRI images based on an end-to-end deep neural network. IEEE Trans. Med. Imaging 37(5), 1127–1139 (2018)
Zhang, J., Baig, S., Wong, A., Haider, M.A., Khalvati, F.: A local ROI-specific atlas-based segmentation of prostate gland and transitional zone in diffusion MRI. J. Comput. Vis. Imaging Syst. 2(1), 1–3 (2016)
Acknowledgment
This project is supported by the Yayasan Universiti Teknologi PETRONAS (YUTP) research fund under grant number 015LC0-292.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Khan, Z., Yahya, N., Alsaih, K., Meriaudeau, F. (2021). Segmentation of Prostate in MRI Images Using Depth Separable Convolution Operations. In: Singh, M., Kang, DK., Lee, JH., Tiwary, U.S., Singh, D., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2020. Lecture Notes in Computer Science(), vol 12615. Springer, Cham. https://doi.org/10.1007/978-3-030-68449-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-68449-5_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68448-8
Online ISBN: 978-3-030-68449-5
eBook Packages: Computer ScienceComputer Science (R0)