Abstract
Prostate cancer is the most common cancer among US men. However, prostate imaging is still challenging despite the advances in multi-parametric magnetic resonance imaging (MRI), which provides both morphologic and functional information pertaining to the pathological regions. Along with whole prostate gland segmentation, distinguishing between the central gland (CG) and peripheral zone (PZ) can guide toward differential diagnosis, since the frequency and severity of tumors differ in these regions; however, their boundary is often weak and fuzzy. This work presents a preliminary study on deep learning to automatically delineate the CG and PZ, aiming at evaluating the generalization ability of convolutional neural networks (CNNs) on two multi-centric MRI prostate datasets. Especially, we compared three CNN-based architectures: SegNet, U-Net, and pix2pix. In such a context, the segmentation performances achieved with/without pre-training were compared in 4-fold cross-validation. In general, U-Net outperforms the other methods, especially when training and testing are performed on multiple datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2018. CA Cancer J. Clin. 68(1), 7–30 (2018)
Rundo, L., Tangherloni, A., Nobile, M.S., Militello, C., Besozzi, D., Mauri, G., Cazzaniga, P.: MedGA: a novel evolutionary method for image enhancement in medical imaging systems. Expert Syst. Appl. 119, 387–399 (2019)
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)
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)
Rundo, L., Militello, C., Russo, G., Garufi, A., Vitabile, S., Gilardi, M.C., Mauri, G.: Automated prostate gland segmentation based on an unsupervised fuzzy c-means clustering technique using multispectral T1w and T2w MR imaging. Information 8(2), 49 (2017)
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)
Niaf, E., Rouvière, O., Mège-Lechevallier, F., Bratan, F., Lartizien, C.: Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI. Phys. Med. Biol. 57(12), 3833 (2012)
Haffner, J., Potiron, E., Bouyé, S., Puech, P., Leroy, X., Lemaitre, L., Villers, A.: Peripheral zone prostate cancers: location and intraprostatic patterns of spread at histopathology. Prostate 69(3), 276–282 (2009)
Selman, S.H.: The McNeal prostate: a review. Urology 78(6), 1224–1228 (2011)
Hoeks, C.M., Barentsz, J.O., Hambrock, T., Yakar, D., Somford, D.M., Heijmink, S.W., et al.: Prostate cancer: multiparametric MR imaging for detection, localization, and staging. Radiology 261(1), 46–66 (2011)
Chang, Y., Chen, R., Yang, Q., Gao, X., Xu, C., Lu, J., Sun, Y.: Peripheral zone volume ratio (PZ-ratio) is relevant with biopsy results and can increase the accuracy of current diagnostic modality. Oncotarget 8(21), 34836 (2017)
Kirby, R., Gilling, R.: Fast Facts: Benign Prostatic Hyperplasia, 7th edn. Health Press Limited, Abingdon, UK (2011)
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI), vol. 9351 of LNCS, pp. 234–241. Springer (2015)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. arXiv preprint arXiv:1611.07004 (2016)
Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B., Liang, J.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016)
Ghose, S., Oliver, A., Martí, R., Lladó, X., Vilanova, J.C., Freixenet, J., et al.: A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images. Comput. Methods Prog. Biomed. 108(1), 262–287 (2012)
Rundo, L., Militello, C., Russo, G., D’Urso, D., Valastro, L.M., Garufi, A., et al.: Fully automatic multispectral MR image segmentation of prostate gland based on the fuzzy c-means clustering algorithm. In: Multidisciplinary Approaches to Neural Computing, vol. 69 of Smart Innovation, Systems and Technologies, pp. 23–37. Springer (2018)
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)
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)
Bevilacqua, V., Brunetti, A., Guerriero, A., Trotta, G.F., Telegrafo, M., Moschetta, M.: A performance comparison between shallow and deeper neural networks supervised classification of tomosynthesis breast lesions images. Cogn. Syst. Res. 53, 3–19 (2019)
Guo, Y., Gao, Y., Shen, D.: Deformable MR prostate segmentation via deep feature learning and sparse patch matching. IEEE Trans. Med. Imaging 35(4), 1077–1089 (2016)
Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: Proceedings of the 4th International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)
Toth, R., Ribault, J., Gentile, J., Sperling, D., Madabhushi, A.: Simultaneous segmentation of prostatic zones using active appearance models with multiple coupled levelsets. Comput. Vis. Image Underst. 117(9), 1051–1060 (2013)
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)
Makni, N., Iancu, A., Colot, O., Puech, P., Mordon, S., Betrouni, N.: Zonal segmentation of prostate using multispectral magnetic resonance images. Med. Phys. 38(11), 6093–6105 (2011)
AlBadawy, E.A., Saha, A., Mazurowski, M.A.: Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing. Med, Phys (2018)
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)
Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Proceedings of COMPSTAT’2010, pp. 177–186. Springer (2010)
Falk, T., Mai, D., Bensch, R., Çiçek, Ö., Abdulkadir, A., Marrakchi, Y., Böhm, A., Deubner, J., Jäckel, Z., Seiwald, K., et al.: U-Net: deep learning for cell counting, detection, and morphometry. Nat. Methods 16(1), 67 (2019)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., et al.: Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge. Med. Image Anal. 18(2), 359–373 (2014)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., et al.: Generative adversarial nets. In: Proceedings of Advances in Neural Information Processing Systems (NIPS), pp. 2672–2680 (2014)
Han, C., Hayashi, H., Rundo, L., Araki, R., Shimoda, W., Muramatsu, S., et al.: GAN-based synthetic brain MR image generation. In: Proceedings of International Symposium on Biomedical Imaging (ISBI), pp. 734–738. IEEE (2018)
Kingma, D., Welling, M.: Auto-encoding variational Bayes. In: Proc. International Conference on Learning Representations (ICLR). arXiv preprint arXiv:1312.6114 (2014)
Acknowledgements
This work was partially supported by the Graduate Program for Social ICT Global Creative Leaders of The University of Tokyo by JSPS. We thank the Cannizzaro Hospital, Catania, Italy, for providing one of the imaging datasets analyzed in this study.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Rundo, L. et al. (2020). CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images: A Cross-Dataset Study. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Neural Approaches to Dynamics of Signal Exchanges. Smart Innovation, Systems and Technologies, vol 151. Springer, Singapore. https://doi.org/10.1007/978-981-13-8950-4_25
Download citation
DOI: https://doi.org/10.1007/978-981-13-8950-4_25
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8949-8
Online ISBN: 978-981-13-8950-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)