ReviewInternational journal of computer assisted radiology and surgery2017
Breast ultrasound image segmentation: a survey.
Review in International journal of computer assisted radiology and surgery, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Saliency-guided deep learning network for automatic tumor bed volume delineation in post-operative breast irradiation.Physics in medicine and biology · 2021Trial
- RPFeaNet: Rethinking Deep Progressive Prompt-Guided Feature Interaction Fusion Network for Medical Ultrasound Image Segmentation.Sensors (Basel, Switzerland) · 2026Article
- Diagnostic and Prognostic Utility of Cell-Surface Vimentin Positive Circulating Tumor Cells in Breast Cancer Using an Automated Negative Selection Platform.Diseases (Basel, Switzerland) · 2026Article
- Development of an Automated CAD System for Lesion Detection in DCE-MRI.Journal of imaging informatics in medicine · 2025Article
- When deep learning is not enough: artificial life as a supplementary tool for segmentation of ultrasound images of breast cancer.Medical & biological engineering & computing · 2025Article
- Deep Learning-Based Medical Ultrasound Image and Video Segmentation Methods: Overview, Frontiers, and Challenges.Sensors (Basel, Switzerland) · 2025Review
- Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis.Bioengineering (Basel, Switzerland) · 2025Review
- Expansive Receptive Field and Local Feature Extraction Network: Advancing Multiscale Feature Fusion for Breast Fibroadenoma Segmentation in Sonography.Journal of imaging informatics in medicine · 2024Article
- Breast tumor segmentation in ultrasound using distance-adapted fuzzy connectedness, convolutional neural network, and active contour.Scientific reports · 2024Article
- A neural network with a human learning paradigm for breast fibroadenoma segmentation in sonography.Biomedical engineering online · 2024Article
- AI-based automated breast cancer segmentation in ultrasound imaging based on Attention Gated Multi ResU-Net.PeerJ. Computer science · 2024Article
- DAU-Net: Dual attention-aided U-Net for segmenting tumor in breast ultrasound images.PloS one · 2024Article
- MS-CFNet: a multi-scale clinical studying-based and feature extraction-guided network for breast fibroadenoma segmentation in ultrasonography.Biomedical engineering letters · 2024Article
- Automatic Segmentation and Assessment of Valvular Regurgitations with Color Doppler Echocardiography Images: A VABC-UNet-Based Framework.Bioengineering (Basel, Switzerland) · 2023Article
- Article
- pH-responsive ratiometric photoacoustic imaging of polyaniline nanoparticle-coated needle for targeted cancer biopsy.Photoacoustics · 2023Article
- The utilization of artificial intelligence applications to improve breast cancer detection and prognosis.Saudi medical journal · 2023Review
- Fully automatic tumor segmentation of breast ultrasound images with deep learning.Journal of applied clinical medical physics · 2023Article
- Trustworthy Breast Ultrasound Image Semantic Segmentation Based on Fuzzy Uncertainty Reduction.Healthcare (Basel, Switzerland) · 2022Article
- Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential.Frontiers in oncology · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeBreast cancer is the most common form of cancer among women worldwide. Ultrasound imaging is one of the most frequently used diagnostic tools to detect and classify abnormalities of the breast. Recently, computer-aided diagnosis (CAD) systems using ultrasound images have been developed to help radiologists to increase diagnosis accuracy. However, accurate ultrasound image segmentation remains a challenging problem due to various ultrasound artifacts. In this paper, we investigate approaches developed for breast ultrasound (BUS) image segmentation.
methodsIn this paper, we reviewed the literature on the segmentation of BUS images according to the techniques adopted, especially over the past 10 years. By dividing into seven classes (i.e., thresholding-based, clustering-based, watershed-based, graph-based, active contour model, Markov random field and neural network), we have introduced corresponding techniques and representative papers accordingly.
resultsWe have summarized and compared many techniques on BUS image segmentation and found that all these techniques have their own pros and cons. However, BUS image segmentation is still an open and challenging problem due to various ultrasound artifacts introduced in the process of imaging, including high speckle noise, low contrast, blurry boundaries, low signal-to-noise ratio and intensity inhomogeneity
conclusionsTo the best of our knowledge, this is the first comprehensive review of the approaches developed for segmentation of BUS images. With most techniques involved, this paper will be useful and helpful for researchers working on segmentation of ultrasound images, and for BUS CAD system developers.
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