Cited by 1,157Medical Image Segmentation TechniquesUltrasound in Clinical ApplicationsMedical Imaging and Analysis

Ultrasound image segmentation: a survey

Open access
Noble, J.A., Boukerroui, D.·2006·IEEE Transactions on Medical Imaging

Abstract

This paper reviews ultrasound segmentation paper methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting ten papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the ultrasound segmentation problem.

Integrity signals

Registry

Crossref · 10.1109/tmi.2006.877092

Cites

256 of 256 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Ultrasound is the most commonly used medical imaging modality for diagnosis and screening in clinical practice.

    “Ultrasound is the most commonly used medical imaging modality for diagnosis and screening in clinical practice [18].”

    From Deep Learning Strategies for Ultrasound in Pregnancy (2020) · cites this paper as [18]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Noble, J. A., & Boukerroui, D.
Affiliations
University of Oxford (GB)
Venue
IEEE Transactions on Medical Imaging
Published
2006-08
Volume
25
Issue
8
Pages
987-1010
ISSN
0278-0062 (ISSN-L)
OpenAlex
W2119249988
Cited by
1,157
References
256
Citations by year
2026: 45 · 2025: 49 · 2024: 40 · 2023: 38 · 2022: 62 · 2021: 45 · 2020: 61 · 2019: 60 · 2018: 49 · 2017: 63 · 2016: 75 · 2015: 68 · 2014: 87 · 2013: 92 · 2012: 80
FWCI
6.28
Type
journal article
Language
en
Open access
Locations
  • IEEE Transactions on Medical Imaging (publishedVersion)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, other-oa)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Ultrasound in Clinical Applications
  • Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
Keywords
ultrasound image segmentation, B-mode ultrasound, medical image segmentation, prior information, clinical application
MeSH terms
Algorithms, Artificial Intelligence, Image Enhancement, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Ultrasonography, Cluster Analysis, Information Storage and Retrieval, Imaging, Three-Dimensional

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review