Automated medical image segmentation techniques
Open accessAbstract
Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. Computed topography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images. The motive is to discuss the problems encountered in segmentation of CT and MR images, and the relative merits and limitations of methods currently available for segmentation of medical images.
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Fully automatic segmentation methods rarely achieve sufficiently accurate and robust results to be clinically useful.
“However, fully automatic segmentation methods rarely achieve sufficiently accurate and robust results to be clinically useful [2].”
From DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2019) · cites this paper as [2]
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“…used in the discussion of a wide range of medical imaging problems, such as imaging segmentation (detection of the limits of a targeted object) [2], registration (visual alignment of anatomic parts on single or multimodality images) [3], and the detection and classification of…”
Background
- Authors
- Sharma, N., & Aggarwal, L.
- Affiliations
- Institute of Medical Sciences (IN)
- Corresponding author
- Sharma, Neeraj
- ORCID
- Sharma, Neeraj
- Venue
- Journal of Medical Physics
- Published
- 2010
- Volume
- 35
- Issue
- 1
- Pages
- 3
- ISSN
- 0971-6203 (ISSN-L)
- PMC
- PMC2825001
- OpenAlex
- W4294305479
- Cited by
- 754
- References
- 28
- Citations by year
- 2026: 29 · 2025: 56 · 2024: 70 · 2023: 89 · 2022: 98 · 2021: 70 · 2020: 64 · 2019: 66 · 2018: 53 · 2017: 30 · 2016: 41 · 2015: 27 · 2014: 16 · 2013: 15 · 2012: 18
- FWCI
- 16.63
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc-sa
- Locations
- Journal of Medical Physics (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Keywords
- medical image segmentation, automatic segmentation, radiotherapy treatment planning, contouring, computed tomography, magnetic resonance imaging, segmentation challenge, segmentation algorithm comparison
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