ReviewJournal of ultrasound2021
Methods for the segmentation and classification of breast ultrasound images: a review.
Review in Journal of ultrasound, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Breast ultrasound images for segmentation and classification using multi-task U-Net.Scientific reports · 2026Article
- Evaluating the Impact of Annotation Expertise on AI-Based Ultrasound Segmentation: A Case Study on Left Atrial Appendage.Cardiovascular engineering and technology · 2026Article
- Development and validation of a semi-automatic radiomics ensemble model for preoperative evaluation of breast masses in mammotome-assisted minimally invasive resection.Gland surgery · 2025Article
- Identifying diversity of patient anatomy through automated image analysis of clinical ultrasounds.Journal of ultrasound · 2024Article
- Article
- DMAeEDNet: Dense Multiplicative Attention Enhanced Encoder Decoder Network for Ultrasound-Based Automated Breast Lesion Segmentation.IEEE access : practical innovations, open solutions · 2024Article
- Review
- Recent Advances in Ultrasound Breast Imaging: From Industry to Clinical Practice.Diagnostics (Basel, Switzerland) · 2023Review
- Explaining a Deep Learning Based Breast Ultrasound Image Classifier with Saliency Maps.Journal of ultrasonography · 2022Article
- Cystic (including atypical) and solid breast lesion classification using the different features of quantitative ultrasound parametric images.International journal of computer assisted radiology and surgery · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeBreast ultrasound (BUS) is one of the imaging modalities for the diagnosis and treatment of breast cancer. However, the segmentation and classification of BUS images is a challenging task. In recent years, several methods for segmenting and classifying BUS images have been studied. These methods use BUS datasets for evaluation. In addition, semantic segmentation algorithms have gained prominence for segmenting medical images.
methodsIn this paper, we examined different methods for segmenting and classifying BUS images. Popular datasets used to evaluate BUS images and semantic segmentation algorithms were examined. Several segmentation and classification papers were selected for analysis and review. Both conventional and semantic methods for BUS segmentation were reviewed.
resultsCommonly used methods for BUS segmentation were depicted in a graphical representation, while other conventional methods for segmentation were equally elucidated.
conclusionsWe presented a review of the segmentation and classification methods for tumours detected in BUS images. This review paper selected old and recent studies on segmenting and classifying tumours in BUS images.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.