ReviewHealthcare (Basel, Switzerland)2022
BUSIS: A Benchmark for Breast Ultrasound Image Segmentation.
Review in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Development and clinical validation of an artificial intelligence based model for thyroid nodule malignancy risk assessment using C-TIRADS guidelines.Frontiers in medicine · 2026Article
- Deep Learning for Tumor Segmentation and Multiclass Classification in Breast Ultrasound Images Using Pretrained Models.Sensors (Basel, Switzerland) · 2025Article
- On the public dissemination and open sourcing of ultrasound resources, datasets and deep learning models.NPJ digital medicine · 2025Article
- XBusNet: Text-Guided Breast Ultrasound Segmentation via Multimodal Vision-Language Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Do Sharpness-Based Optimizers Improve Generalization in Medical Image Analysis?IEEE access : practical innovations, open solutions · 2025Article
- Deep Learning for Describing Breast Ultrasound Images with BI-RADS Terms.Journal of imaging informatics in medicine · 2024Article
- Enhancing Surgical Guidance: Deep Learning-Based Liver Vessel Segmentation in Real-Time Ultrasound Video Frames.Cancers · 2024Article
- Breast tumor segmentation in ultrasound using distance-adapted fuzzy connectedness, convolutional neural network, and active contour.Scientific reports · 2024Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- Federated learning for medical image analysis: A survey.Pattern recognition · 2024Article
- A Review of Artificial Intelligence in Breast Imaging.Tomography (Ann Arbor, Mich.) · 2024Review
- Attention gate and dilation U-shaped network (GDUNet): an efficient breast ultrasound image segmentation network with multiscale information extraction.Quantitative imaging in medicine and surgery · 2024Article
- An efficient deep learning strategy for accurate and automated detection of breast tumors in ultrasound image datasets.Frontiers in oncology · 2024Article
- Application and prospects of AI-based radiomics in ultrasound diagnosis.Visual computing for industry, biomedicine, and art · 2023Review
- Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Blood flow on ultrasound imaging is a predictor of lump margin status in breast-conserving patients: a retrospective matching study.European journal of medical research · 2023Article
- Article
- Fully automatic tumor segmentation of breast ultrasound images with deep learning.Journal of applied clinical medical physics · 2023Article
- BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations.IEEE access : practical innovations, open solutions · 2023Article
- Post-pandemic healthcare for COVID-19 vaccine: Tissue-aware diagnosis of cervical lymphadenopathy via multi-modal ultrasound semantic segmentation.Applied soft computing · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Breast ultrasound (BUS) image segmentation is challenging and critical for BUS computer-aided diagnosis (CAD) systems. Many BUS segmentation approaches have been studied in the last two decades, but the performances of most approaches have been assessed using relatively small private datasets with different quantitative metrics, which results in a discrepancy in performance comparison. Therefore, there is a pressing need for building a benchmark to compare existing methods using a public dataset objectively, to determine the performance of the best breast tumor segmentation algorithm available today, and to investigate what segmentation strategies are valuable in clinical practice and theoretical study. In this work, a benchmark for B-mode breast ultrasound image segmentation is presented. In the benchmark, (1) we collected 562 breast ultrasound images and proposed standardized procedures to obtain accurate annotations using four radiologists; (2) we extensively compared the performance of 16 state-of-the-art segmentation methods and demonstrated that most deep learning-based approaches achieved high dice similarity coefficient values (DSC ≥ 0.90) and outperformed conventional approaches; (3) we proposed the losses-based approach to evaluate the sensitivity of semi-automatic segmentation to user interactions; and (4) the successful segmentation strategies and possible future improvements were discussed in details.
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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.