The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges
Open accessAbstract
Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions.
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Cites
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Cited by
TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation
Zhang, Y., Liu, H., & Hu, Q. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-87193-2_2 · Cited by 1,312
A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia
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Zhou, S. K., Greenspan, H., Davatzikos, C., Duncan, J. S., Van Ginneken, B., Madabhushi, A., Prince, J. L., Rueckert, D., & Summers, R. M. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3054390 · Cited by 1,118
Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
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“Huang et al. (2018b) use their method for colorectal tumor segmentation in MRI volumes.”
BackgroundMethodConvolutional neural networks in medical image understanding: a survey
Sarvamangala, D. R., & Kulkarni, R. V. (2022) · Evolutionary Intelligence · doi:10.1007/s12065-020-00540-3 · Cited by 917
Metadata
- Authors
- Hesamian, M. H., Jia, W., He, X., & Kennedy, P.
- Venue
- Journal of Digital Imaging
- Published
- 2019-05-29
- Volume
- 32
- Issue
- 4
- Pages
- 582-596
- ISSN
- 0897-1889 (ISSN-L)
- 1618-727X
- OpenAlex
- W2947263797
- Cited by
- 1,631
- References
- 95
- FWCI
- 78.65
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Hesamian, M. H., Jia, W., He, X., & Kennedy, P. (2019). Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges. Journal of Digital Imaging. https://doi.org/10.1007/s10278-019-00227-x
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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.
