The DOI matches its Crossref record; 114 of the 114 references it cites were found in a registry or an index.
A Review of Deep-Learning-Based Medical Image Segmentation Methods
Open accessAbstract
As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. Now it has become an important research direction in the field of computer vision. With the rapid development of deep learning, medical image processing based on deep convolutional neural networks has become a research hotspot. This paper focuses on the research of medical image segmentation based on deep learning. First, the basic ideas and characteristics of medical image segmentation based on deep learning are introduced. By explaining its research status and summarizing the three main methods of medical image segmentation and their own limitations, the future development direction is expanded. Based on the discussion of different pathological tissues and organs, the specificity between them and their classic segmentation algorithms are summarized. Despite the great achievements of medical image segmentation in recent years, medical image segmentation based on deep learning has still encountered difficulties in research. For example, the segmentation accuracy is not high, the number of medical images in the data set is small and the resolution is low. The inaccurate segmentation results are unable to meet the actual clinical requirements. Aiming at the above problems, a comprehensive review of current medical image segmentation methods based on deep learning is provided to help researchers solve existing problems.
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Self-citationRetractedMedical multimodal large language models: A systematic review
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Metadata
- Authors
- Liu, X., Song, L., Liu, S., & Zhang, Y.
- Venue
- Sustainability
- Publisher
- MDPI AG
- Published
- 2021-01-25
- Volume
- 13
- Issue
- 3
- Pages
- 1224
- ISSN
- 2071-1050 (ISSN-L)
- OpenAlex
- W3123982987
- Cited by
- 868
- References
- 114
- FWCI
- 49.56
- Type
- journal article
- Language
- en
- Open access
- gold
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