The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Lung CT Image Segmentation Using Deep Neural Networks
Open accessAbstract
Lung CT image segmentation is a necessary initial step for lung image analysis, it is a prerequisite step to provide an accurate lung CT image analysis such as lung cancer detection. In this work, we propose a lung CT image segmentation using the U-net architecture, one of the most used architectures in deep learning for image segmentation. The architecture consists of a contracting path to extract high-level information and a symmetric expanding path that recovers the information needed. This network can be trained end-to-end from very few images and outperforms many methods. Experimental results show an accurate segmentation with 0.9502 Dice-Coefficient index.
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Brain tumor segmentation with Deep Neural Networks
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., & Larochelle, H. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.05.004 · Cited by 3,278
A survey on deep learning techniques for image and video semantic segmentation
Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., Martinez-Gonzalez, P., & Garcia-Rodriguez, J. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.05.018 · Cited by 1,490
Deep learning for brain MRI segmentation: state of the art and future directions
Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L., & Erickson, B. J. (2017) · Journal of Digital Imaging · doi:10.1007/s10278-017-9983-4 · Cited by 1,129
Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images
Hu, S., Hoffman, E. A., & Reinhardt, J. M. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.929615 · Cited by 940
Computer analysis of computed tomography scans of the lung: a survey
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Cited by
CE-Net: Context Encoder Network for 2D Medical Image Segmentation
Gu, Z., Cheng, J., Fu, H., Zhou, K., Hao, H., Zhao, Y., Zhang, T., Gao, S., & Liu, J. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2903562 · Cited by 2,267
Toraman, S., Alakus, T. B., & Turkoglu, I. (2020) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110122 · Cited by 327
COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet
Saood, A., & Hatem, I. (2021) · BMC Medical Imaging · doi:10.1186/s12880-020-00529-5 · Cited by 285
A Survey and Taxonomy of FPGA-based Deep Learning Accelerators
Blaiech, A. G., Ben Khalifa, K., Valderrama, C., Fernandes, M. A. C., & Bedoui, M. H. (2019) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2019.01.007 · Cited by 88
Three-stage segmentation of lung region from CT images using deep neural networks
Osadebey, M., Andersen, H. K., Waaler, D., Fossaa, K., Martinsen, A. C. T., & Pedersen, M. (2021) · BMC Medical Imaging · doi:10.1186/s12880-021-00640-1 · Cited by 34
Metadata
- Authors
- Ait Skourt, B., El Hassani, A., & Majda, A.
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2018
- Volume
- 127
- Pages
- 109-113
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W2793954249
- Cited by
- 346
- References
- 12
- FWCI
- 9.85
- Type
- conference paper
- Language
- en
- Open access
- diamond
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
