Abstract
Microscopic images from multiple modalities can produce plentiful experimental information. In practice, biological or physical constraints under a given observation period may prevent researchers from acquiring enough microscopic scanning. Recent studies demonstrate that image synthesis is one of the popular approaches to release such constraints. Nonetheless, most existing synthesis approaches only translate images from the source domain to the target domain without solid geometric associations. To embrace this challenge, we propose an innovative model architecture, BANIS, to synthesize diversified microscopic images from multi-source domains with distinct geometric features. The experimental outcomes indicate that BANIS successfully synthesizes favorable image pairs on C. elegans microscopy embryonic images. To the best of our knowledge, BANIS is the first application to synthesize microscopic images that associate distinct spatial geometric features from multi-source domains.
Keywords
- Cross domain synthesis
- Bidirectional adversarial networks
- Multi-source microscopic images
- Geometric matching
This study is supported by an NIH research project grants (R01GM097576).
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Our code is available on Github at: https://github.com/junzhuang-code/BANIS.
References
Chartsias, A., Joyce, T., Giuffrida, M.V., Tsaftaris, S.A.: Multimodal MR synthesis via modality-invariant latent representation. IEEE Trans. Med. Imaging 37, 803–814 (2017)
Dai, M.Q., Zheng, W., Huang, Z., Yung, L.Y.L.: Aqueous phase synthesis of widely tunable photoluminescence emission CdtTe/CdS core/shell quantum dots under a totally ambient atmosphere. J. Mater. Chem. 22, 16336–16345 (2012)
Dar, S.U., Yurt, M., Karacan, L., Erdem, A., Erdem, E., Çukur, T.: Image synthesis in multi-contrast MRI with conditional generative adversarial networks. IEEE Trans. Med. Imaging 38, 2375–2388 (2019)
Donahue, J., Krähenbühl, P., Darrell, T.: Adversarial feature learning. arXiv preprint arXiv:1605.09782 (2016)
Femmam, S., Iles, A., Bessaid, A.: Optimizing magnetic resonance imaging reconstructions. Electron. Imaging Signal Process. J. SPIE Newsroom (2015)
Gao, L., Pan, H., Han, J., Xie, X., Zhang, Z., Zhai, X.: Corner detection and matching methods for brain medical image classification. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE (2016)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Huo, Y., et al.: SynSeg-Net: synthetic segmentation without target modality ground truth. IEEE Trans. Med. Imaging 38, 1016–1025 (2018)
Jog, A., Carass, A., Roy, S., Pham, D.L., Prince, J.L.: MR image synthesis by contrast learning on neighborhood ensembles. Med. Image Anal. 24, 63–76 (2015)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
Lee, J., Carass, A., Jog, A., Zhao, C., Prince, J.L.: Multi-atlas-based CT synthesis from conventional MRI with patch-based refinement for MRI-based radiotherapy planning. In: Medical Imaging 2017: Image Processing. International Society for Optics and Photonics (2017)
Liu, H., Cocea, M.: Granular computing-based approach of rule learning for binary classification. Granular Comput. 4, 275–283 (2019)
Lu, C., Mandal, M.: Automated analysis and diagnosis of skin melanoma on whole slide histopathological images. Pattern Recogn. 48, 2738–2750 (2015)
Meinel, L.A., Stolpen, A.H., Berbaum, K.S., Fajardo, L.L., Reinhardt, J.M.: Breast MRI lesion classification: improved performance of human readers with a backpropagation neural network computer-aided diagnosis (CAD) system. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine (2007)
Miller, M.I., Christensen, G.E., Amit, Y., Grenander, U.: Mathematical textbook of deformable neuroanatomies. Proceedings of the National Academy of Sciences (1993)
Mou, L., et al.: CS2-net: deep learning segmentation of curvilinear structures in medical imaging. Med. Image Anal. 67, 101874 (2020)
Nie, D., et al.: Medical image synthesis with deep convolutional adversarial networks. IEEE Trans. Biomed. Eng. 65, 2720–2730 (2018)
Parisi, L., RaviChandran, N., Lanzillotta, M.: Supervised machine learning for aiding diagnosis of knee osteoarthritis: a systematic review and meta-analysis (2020)
Peng, C., Pan, N., Xie, Z., Liu, L., Xiang, J., Liu, C.: Determination of bisphenol a by a gold nanoflower enhanced enzyme-linked immunosorbent assay. Anal. Lett. 49, 1492–1501 (2016)
Su, R., Hu, Y.: Medical Imaging and Computer-Aided Diagnosis. Springer (2020)
Vakharia, V.N., et al.: The effect of vascular segmentation methods on stereotactic trajectory planning for drug-resistant focal epilepsy: a retrospective cohort study. World Neurosurg. X 4, 100057 (2019)
Wang, D., Lu, Z., Xu, Y., Wang, Z., Santella, A., Bao, Z.: Cellular structure image classification with small targeted training samples. IEEE Access 7, 148967–148974 (2019)
Wei, H., et al.: Precise targeting of the globus pallidus internus with quantitative susceptibility mapping for deep brain stimulation surgery. J. Neurosurg. 133, 1605–1611 (2019)
Yuan, Y., Huang, W., Wang, X., Xu, H., Zuo, H., Su, R.: Automated accurate registration method between UAV image and google satellite map. Multimed. Tools Appl. 79, 16573–16591 (2019)
Zamzmi, G., Rajaraman, S., Antani, S.: Accelerating super-resolution and visual task analysis in medical images. Appl. Sci. 10, 4282 (2020)
Zhang, Y.D., Govindaraj, V.V., Tang, C., Zhu, W., Sun, J.: High performance multiple sclerosis classification by data augmentation and AlexNet transfer learning model. J. Med. Imaging Health Inform. 9, 2012–2021 (2019)
Zhao, Y., Rada, L., Chen, K., Harding, S.P., Zheng, Y.: Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images. IEEE Trans. Med. Imaging 34, 1797–1807 (2015)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Zhuang, J., Gao, M., Hasan, M.A.: Lighter U-net for segmenting white matter hyperintensities in MR images. In: Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhuang, J., Wang, D. (2022). Geometrically Matched Multi-source Microscopic Image Synthesis Using Bidirectional Adversarial Networks. In: Su, R., Zhang, YD., Liu, H. (eds) Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021). MICAD 2021. Lecture Notes in Electrical Engineering, vol 784. Springer, Singapore. https://doi.org/10.1007/978-981-16-3880-0_9
Download citation
DOI: https://doi.org/10.1007/978-981-16-3880-0_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-3879-4
Online ISBN: 978-981-16-3880-0
eBook Packages: EngineeringEngineering (R0)