More Web Proxy on the site http://driver.im/

Article

VISA-FSS: A Volume-Informed Self Supervised Approach for Few-Shot 3D Segmentation

Authors:

Mohammad Mozafari,

Adeleh Bitarafan,

Mohammad Farid Azampour,

Mahdieh Soleymani Baghshah,

Nassir NavabAuthors Info & Claims

Medical Image Computing and Computer Assisted Intervention – MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part II

Pages 112 - 122

https://doi.org/10.1007/978-3-031-43895-0_11

Published: 08 October 2023 Publication History

Abstract

Few-shot segmentation (FSS) models have gained popularity in medical imaging analysis due to their ability to generalize well to unseen classes with only a small amount of annotated data. A key requirement for the success of FSS models is a diverse set of annotated classes as the base training tasks. This is a difficult condition to meet in the medical domain due to the lack of annotations, especially in volumetric images. To tackle this problem, self-supervised FSS methods for 3D images have been introduced. However, existing methods often ignore intra-volume information in 3D image segmentation, which can limit their performance. To address this issue, we propose a novel self-supervised volume-aware FSS framework for 3D medical images, termed VISA-FSS. In general, VISA-FSS aims to learn continuous shape changes that exist among consecutive slices within a volumetric image to improve the performance of 3D medical segmentation. To achieve this goal, we introduce a volume-aware task generation method that utilizes consecutive slices within a 3D image to construct more varied and realistic self-supervised FSS tasks during training. In addition, to provide pseudo-labels for consecutive slices, a novel strategy is proposed that propagates pseudo-labels of a slice to its adjacent slices using flow field vectors to preserve anatomical shape continuity. In the inference time, we then introduce a volumetric segmentation strategy to fully exploit the inter-slice information within volumetric images. Comprehensive experiments on two common medical benchmarks, including abdomen CT and MRI, demonstrate the effectiveness of our model over state-of-the-art methods. Code is available at https://github.com/sharif-ml-lab/visa-fss

References

[1]

Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: Slic superpixels. Tech. rep. (2010)

[2]

Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: An unsupervised learning model for deformable medical image registration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9252–9260 (2018)

[3]

Bitarafan, A., Azampour, M.F., Bakhtari, K., Soleymani Baghshah, M., Keicher, M., Navab, N.: Vol2flow: segment 3d volumes using a sequence of registration flows. In: Medical Image Computing and Computer Assisted Intervention-MICCAI 2022: 25th International Conference, Proceedings, Part IV, pp. 609–618. Springer (2022).

Digital Library

[4]

Bitarafan A, Nikdan M, and Baghshah MS 3d image segmentation with sparse annotation by self-training and internal registration IEEE J. Biomed. Health Inform. 2020 25 7 2665-2672

[5]

Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607. PMLR (2020)

[6]

Chen X et al. A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy Radiother. Oncol. 2021 160 175-184

[7]

Denner S et al. Crimi A, Bakas S, et al. Spatio-temporal learning from longitudinal data for multiple sclerosis lesion segmentation Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries 2021 Cham Springer 111-121

[8]

Ding, H., Sun, C., Tang, H., Cai, D., Yan, Y.: Few-shot medical image segmentation with cycle-resemblance attention. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2488–2497 (2023)

[9]

Farshad, A., Makarevich, A., Belagiannis, V., Navab, N.: Metamedseg: volumetric meta-learning for few-shot organ segmentation. In: Domain Adaptation and Representation Transfer 2022, pp. 45–55. Springer (2022).

Digital Library

[10]

Felzenszwalb PF and Huttenlocher DP Efficient graph-based image segmentation Int. J. Comput. Vision 2004 59 167-181

Digital Library

[11]

Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp. 1126–1135. PMLR (2017)

[12]

Hesamian MH, Jia W, He X, and Kennedy P Deep learning techniques for medical image segmentation: achievements and challenges J. Digit. Imaging 2019 32 582-596

[13]

Hospedales T, Antoniou A, Micaelli P, and Storkey A Meta-learning in neural networks: a survey IEEE Trans. Pattern Anal. Mach. Intell. 2021 44 9 5149-5169

[14]

Kavur AE et al. Chaos challenge-combined (ct-mr) healthy abdominal organ segmentation Med. Image Anal. 2021 69

[15]

Landman, B., Xu, Z., Igelsias, J., Styner, M., Langerak, T., Klein, A.: Miccai multi-atlas labeling beyond the cranial vault-workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault-Workshop Challenge. vol. 5, p. 12 (2015)

[16]

Li X, Chen H, Qi X, Dou Q, Fu CW, and Heng PA H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes IEEE Trans. Med. Imaging 2018 37 12 2663-2674

[17]

Lutnick B An integrated iterative annotation technique for easing neural network training in medical image analysis Nat. Mach. Intell. 2019 1 2 112-119

[18]

Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)

[19]

Ouyang C, Biffi C, Chen C, Kart T, Qiu H, and Rueckert D Vedaldi A, Bischof H, Brox T, and Frahm J-M Self-supervision with superpixels: training few-shot medical image segmentation without annotation Computer Vision – ECCV 2020 2020 Cham Springer 762-780

Digital Library

[20]

Ouyang C, Kamnitsas K, Biffi C, Duan J, Rueckert D, et al. Shen D et al. Data efficient unsupervised domain adaptation for cross-modality image segmentation Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 2019 Cham Springer 669-677

Digital Library

[21]

Roy AG, Siddiqui S, Pölsterl S, Navab N, and Wachinger C Squeeze & excite’guided few-shot segmentation of volumetric images Med. Image Anal. 2020 59

[22]

Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems 30 (2017)

[23]

Tang, H., Liu, X., Sun, S., Yan, X., Xie, X.: Recurrent mask refinement for few-shot medical image segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3918–3928 (2021)

[24]

Tsochatzidis L, Koutla P, Costaridou L, and Pratikakis I Integrating segmentation information into CNN for breast cancer diagnosis of mammographic masses Comput. Methods Programs Biomed. 2021 200

[25]

Wang, K., Liew, J.H., Zou, Y., Zhou, D., Feng, J.: Panet: few-shot image semantic segmentation with prototype alignment. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9197–9206 (2019)

[26]

Zhao, A., Balakrishnan, G., Durand, F., Guttag, J.V., Dalca, A.V.: Data augmentation using learned transformations for one-shot medical image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8543–8553 (2019)

Cited By

Yeganeh YGüvercin GXiao RAbuzer AAdeli EFarshad ANavab N(2023)SCOPE: Structural Continuity Preservation for Retinal Vessel SegmentationGraphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology10.1007/978-3-031-55088-1_1(3-13)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-55088-1_1

Recommendations

Self-supervised Pretraining for 2D Medical Image Segmentation
Computer Vision – ECCV 2022 Workshops
Abstract
Supervised machine learning provides state-of-the-art solutions to a wide range of computer vision problems. However, the need for copious labelled training data limits the capabilities of these algorithms in scenarios where such input is scarce ...
Self-supervised few-shot medical image segmentation with spatial transformations
Abstract
Deep learning-based segmentation models often struggle to achieve optimal performance when encountering new, unseen semantic classes. Their effectiveness hinges on vast amounts of annotated data and high computational resources for training. ...
Boundary-Enhanced Self-supervised Learning for Brain Structure Segmentation
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022
Abstract
To alleviate the demand for a large amount of annotated data by deep learning methods, this paper explores self-supervised learning (SSL) for brain structure segmentation. Most SSL methods treat all pixels equally, failing to emphasize the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings

Medical Image Computing and Computer Assisted Intervention – MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part II

Oct 2023

827 pages

ISBN:978-3-031-43894-3

DOI:10.1007/978-3-031-43895-0

Editors:
Hayit Greenspan
https://ror.org/04mhzgx49Icahn School of Medicine, Mount Sinai, NYC, NY, USA, Tel Aviv University, Tel Aviv, Israel
,
Anant Madabhushi
https://ror.org/03czfpz43Emory University, Atlanta, GA, USA
,
Parvin Mousavi
https://ror.org/02y72wh86Queen’s University, Kingston, ON, Canada
,
Septimiu Salcudean
https://ror.org/03rmrcq20The University of British Columbia, Vancouver, BC, Canada
,
James Duncan
https://ror.org/03v76x132Yale University, New Haven, CT, USA
,
Tanveer Syeda-Mahmood
IBM Research, San Jose, CA, USA
,
Russell Taylor
https://ror.org/00za53h95Johns Hopkins University, Baltimore, MD, USA

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 08 October 2023

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 15 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Yeganeh YGüvercin GXiao RAbuzer AAdeli EFarshad ANavab N(2023)SCOPE: Structural Continuity Preservation for Retinal Vessel SegmentationGraphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology10.1007/978-3-031-55088-1_1(3-13)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-55088-1_1

View Options

View options

Media

Figures

Other

Tables

View Table of Contents