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Erschienen in:
Tissue Classification to Support Local Active Delineation of Brain Tumors Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Using a Conditional Generative Adversarial Network (cGAN) for Prostate Segmentation

verfasst von : Amelie Grall, Azam Hamidinekoo, Paul Malcolm, Reyer Zwiggelaar

Erschienen in: Medical Image Understanding and Analysis

Verlag: Springer International Publishing

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Abstract

Prostate cancer is the second most commonly diagnosed cancer among men and currently multi-parametric MRI is a promising imaging technique used for clinical workup of prostate cancer. Accurate detection and localisation of the prostate tissue boundary on various MRI scans can be helpful for obtaining a region of interest for Computer Aided Diagnosis systems. In this paper, we present a fully automated detection and segmentation pipeline using a conditional Generative Adversarial Network (cGAN). We investigated the robustness of the cGAN model against adding Gaussian noise or removing noise from the training data. Based on the detection and segmentation metrics, de-noising did not show a significant improvement. However, by including noisy images in the training data, the detection and segmentation performance was improved in each 3D modality, which resulted in comparable to state-of-the-art results.

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Vorheriges Kapitel Tissue Classification to Support Local Active Delineation of Brain Tumors
Nächstes Kapitel A Novel Application of Multifractal Features for Detection of Microcalcifications in Digital Mammograms
Fußnoten
Literatur
1.
Birkbeck, N., Zhang, J., Requardt, M., Kiefer, B., Gall, P., Kevin Zhou, S.: Region-specific hierarchical segmentation of MR prostate using discriminative learning. In: MICCAI Grand Challenge: Prostate MR Image Segmentation, vol. 2012 (2012)
2.
3.
Chen, G., Zhang, P., Wu, Y., Shen, D., Yap, P.T.: Denoising magnetic resonance images using collaborative non-local means. Neurocomputing 177, 215–227 (2016)CrossRef
4.
Cheng, R., et al.: Active appearance model and deep learning for more accurate prostate segmentation on MRI. In: Medical Imaging 2016: Image Processing, vol. 9784, p. 97842I. International Society for Optics and Photonics (2016)
5.
Coupé, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., Barillot, C.: An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans. Med. Imaging 27(4), 425–441 (2008)CrossRef
6.
Gao, Q., Rueckert, D., Edwards, P.: An automatic multi-atlas based prostate segmentation using local appearance-specific atlases and patch-based voxel weighting. In: MICCAI Grand Challenge: Prostate MR Image Segmentation, vol. 2012a (2012)
7.
Ghose, S., et al.: A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images. Comput. Methods Programs Biomed. 108(1), 262–287 (2012)CrossRef
8.
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
9.
Hegde, J.V., et al.: Multiparametric MRI of prostate cancer: an update on state-of-the-art techniques and their performance in detecting and localizing prostate cancer. J. Magn. Reson. Imaging 37(5), 1035–1054 (2013)CrossRef
10.
Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)MathSciNetCrossRef
11.
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. arXiv preprint (2017)
12.
Jemal, A., Bray, F., Center, M.M., Ferlay, J., Ward, E., Forman, D.: Global cancer statistics. CA Cancer J. Clin. 61(2), 69–90 (2011)CrossRef
13.
Karimi, D., Samei, G., Shao, Y., Salcudean, T.: A novel deep learning-based method for prostate segmentation in T2-weighted magnetic resonance imaging (2017)
14.
Kirschner, M., Jung, F., Wesarg, S.: Automatic prostate segmentation in MR images with a probabilistic active shape model. In: MICCAI Grand Challenge: Prostate MR Image Segmentation, vol. 2012 (2012)
15.
Litjens, G., Karssemeijer, N., Huisman, H.: A multi-atlas approach for prostate segmentation in MR images. In: MICCAI Grand Challenge: Prostate MR Image Segmentation, vol. 2012 (2012)
16.
Litjens, G., et al.: Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge. Med. Image Anal. 18(2), 359–373 (2014)CrossRef
17.
Maan, B., van der Heijden, F.: Prostate MR image segmentation using 3D active appearance models. In: MICCAI Grand Challenge: Prostate MR Image Segmentation, vol. 2012 (2012)
18.
Malmberg, F., Strand, R., Kullberg, J., Nordenskjöld, R., Bengtsson, E.: Smart paint a new interactive segmentation method applied to MR prostate segmentation. In: MICCAI Grand Challenge: Prostate MR Image Segmentation, vol. 2012 (2012)
19.
Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)
20.
21.
Ou, Y., Doshi, J., Erus, G., Davatzikos, C.: Multi-atlas segmentation of the prostate: a zooming process with robust registration and atlas selection. In: MICCAI Grand Challenge: Prostate MR Image Segmentation, vol. 7, pp. 1–7 (2012)
22.
Pasquier, D., Lacornerie, T., Vermandel, M., Rousseau, J., Lartigau, E., Betrouni, N.: Automatic segmentation of pelvic structures from magnetic resonance images for prostate cancer radiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 68(2), 592–600 (2007)CrossRef
23.
24.
Toth, R., Madabhushi, A.: Deformable landmark-free active appearance models: application to segmentation of multi-institutional prostate MRI data. In: MICCAI Grand Challenge: Prostate MR Image Segmentation, vol. 2012 (2012)
25.
Vincent, G., Guillard, G., Bowes, M.: Fully automatic segmentation of the prostate using active appearance models. In: MICCAI Grand Challenge: Prostate MR Image Segmentation, vol. 2012 (2012)
26.
Yu, L., 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, pp. 66–72 (2017)
27.
Yuan, J., Qiu, W., Ukwatta, E., Rajchl, M., Sun, Y., Fenster, A.: An efficient convex optimization approach to 3D prostate MRI segmentation with generic star shape prior. In: MICCAI Grand Challenge: Prostate MR Image Segmentation, vol. 7512, pp. 82–89 (2012)
Metadaten
Titel
Using a Conditional Generative Adversarial Network (cGAN) for Prostate Segmentation
verfasst von
Amelie Grall
Azam Hamidinekoo
Paul Malcolm
Reyer Zwiggelaar
Copyright-Jahr
2020
Buch
Medical Image Understanding and Analysis

Print ISBN: 978-3-030-39342-7

Electronic ISBN: 978-3-030-39343-4

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-39343-4_2