Article

PAN: Projective Adversarial Network for Medical Image Segmentation

Authors:

Naji Khosravan,

Aliasghar Mortazi,

Michael Wallace,

Ulas BagciAuthors Info & Claims

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part VI

Pages 68 - 76

https://doi.org/10.1007/978-3-030-32226-7_8

Published: 13 October 2019 Publication History

Abstract

Adversarial learning has been proven to be effective for capturing long-range and high-level label consistencies in semantic segmentation. Unique to medical imaging, capturing 3D semantics in an effective yet computationally efficient way remains an open problem. In this study, we address this computational burden by proposing a novel projective adversarial network, called PAN, which incorporates high-level 3D information through 2D projections. Furthermore, we introduce an attention module into our framework that helps for a selective integration of global information directly from our segmentor to our adversarial network. For the clinical application we chose pancreas segmentation from CT scans. Our proposed framework achieved state-of-the-art performance without adding to the complexity of the segmentor.

References

[1]

Cai, J., Lu, L., Xie, Y., Xing, F., Yang, L.: Improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function. arXiv preprint arXiv:1707.04912 (2017)

[2]

Chen LC, Papandreou G, Kokkinos I, Murphy K, and Yuille AL Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs IEEE Trans. Pattern Anal. Mach. Intell. 2018 40 4 834-848

[3]

Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)

[4]

Chen L-C, Zhu Y, Papandreou G, Schroff F, and Adam H Ferrari V, Hebert M, Sminchisescu C, and Weiss Y Encoder-decoder with atrous separable convolution for semantic image segmentation Computer Vision – ECCV 2018 2018 Cham Springer 833-851

Digital Library

[5]

Dai W, Dong N, Wang Z, Liang X, Zhang H, Xing EP, et al. Stoyanov D et al. SCAN: structure correcting adversarial network for organ segmentation in chest X-rays Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 2018 Cham Springer 263-273

Digital Library

[6]

Gadelha, M., Maji, S., Wang, R.: 3D shape induction from 2D views of multiple objects. In: 2017 International Conference on 3D Vision (3DV), pp. 402–411. IEEE (2017)

[7]

Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

[8]

Luc, P., Couprie, C., Chintala, S., Verbeek, J.: Semantic segmentation using adversarial networks. arXiv preprint arXiv:1611.08408 (2016)

[9]

Oktay, O., et al.: Attention U-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)

[10]

Rezaei M et al. Crimi A, Bakas S, Kuijf H, Menze B, Reyes M, et al. A conditional adversarial network for semantic segmentation of brain tumor Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries 2018 Cham Springer 241-252

[11]

Roth HR et al. Navab N, Hornegger J, Wells WM, Frangi AF, et al. DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 2015 Cham Springer 556-564

Digital Library

[12]

Roth HR, Lu L, Farag A, Sohn A, and Summers RM Ourselin S, Joskowicz L, Sabuncu MR, Unal G, and Wells W Spatial aggregation of holistically-nested networks for automated pancreas segmentation Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016 2016 Cham Springer 451-459

Digital Library

[13]

Roth HR et al. Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation Med. Image Anal. 2018 45 94-107

[14]

Xue Y, Xu T, Zhang H, Long LR, and Huang X Segan: adversarial network with multi-scale

L_{1}

loss for medical image segmentation Neuroinformatics 2018 16 3–4 383-392

[15]

Yi, X., Walia, E., Babyn, P.: Generative adversarial network in medical imaging: A review. arXiv preprint arXiv:1809.07294 (2018)

[16]

Yu, Q., Xie, L., Wang, Y., Zhou, Y., Fishman, E.K., Yuille, A.L.: Recurrent saliency transformation network: incorporating multi-stage visual cues for small organ segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8280–8289 (2018)

[17]

Zheng, S., et al.: Conditional random fields as recurrent neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1529–1537 (2015)

[18]

Zhou Y, Xie L, Shen W, Wang Y, Fishman EK, and Yuille AL Descoteaux M, Maier-Hein L, Franz A, Jannin P, Collins DL, and Duchesne S A fixed-point model for pancreas segmentation in abdominal CT scans Medical Image Computing and Computer Assisted Intervention – MICCAI 2017 2017 Cham Springer 693-701

Digital Library

Cited By

Demir UZhang ZWang BAntalek MKeles EJha DBorhani ALadner DBagci U(2022)Transformer Based Generative Adversarial Network for Liver SegmentationImage Analysis and Processing. ICIAP 2022 Workshops10.1007/978-3-031-13324-4_29(340-347)Online publication date: 23-May-2022
https://dl.acm.org/doi/10.1007/978-3-031-13324-4_29
Pawar KZhong SGoonatillake DEgan GChen Z(2021)Orthogonal-Nets: A Large Ensemble of 2D Neural Networks for 3D Brain Tumor SegmentationBrainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries10.1007/978-3-031-09002-8_5(54-67)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1007/978-3-031-09002-8_5
Proietto Salanitri FBellitto GIrmakci IPalazzo SBagci USpampinato C(2021)Hierarchical 3D Feature Learning forPancreas SegmentationMachine Learning in Medical Imaging10.1007/978-3-030-87589-3_25(238-247)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1007/978-3-030-87589-3_25

Index Terms

PAN: Projective Adversarial Network for Medical Image Segmentation

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Guide Proceedings

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part VI

Oct 2019

894 pages

ISBN:978-3-030-32225-0

DOI:10.1007/978-3-030-32226-7

Editors:
Dinggang Shen
University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
,
Tianming Liu
University of Georgia, Athens, GA, USA
,
Terry M. Peters
Western University, London, ON, Canada
,
Lawrence H. Staib
Yale University, New Haven, CT, USA
,
Caroline Essert
University of Strasbourg, Illkirch, France
,
Sean Zhou
United Imaging Intelligence, Shanghai, China
,
Pew-Thian Yap
University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
,
Ali Khan
Western University, London, ON, Canada

© Springer Nature Switzerland AG 2019.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 13 October 2019

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Cited By

Demir UZhang ZWang BAntalek MKeles EJha DBorhani ALadner DBagci U(2022)Transformer Based Generative Adversarial Network for Liver SegmentationImage Analysis and Processing. ICIAP 2022 Workshops10.1007/978-3-031-13324-4_29(340-347)Online publication date: 23-May-2022
https://dl.acm.org/doi/10.1007/978-3-031-13324-4_29
Pawar KZhong SGoonatillake DEgan GChen Z(2021)Orthogonal-Nets: A Large Ensemble of 2D Neural Networks for 3D Brain Tumor SegmentationBrainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries10.1007/978-3-031-09002-8_5(54-67)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1007/978-3-031-09002-8_5
Proietto Salanitri FBellitto GIrmakci IPalazzo SBagci USpampinato C(2021)Hierarchical 3D Feature Learning forPancreas SegmentationMachine Learning in Medical Imaging10.1007/978-3-030-87589-3_25(238-247)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1007/978-3-030-87589-3_25

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