ArticleMedical & biological engineering & computing2022
Real-time denoising of ultrasound images based on deep learning.
Article in Medical & biological engineering & computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging.Communications engineering · 2026Review
- BDNet: A Real-Time Biomedical Image Denoising Network with Gradient Information Enhancement Loss.Biosensors · 2026Article
- Predicting Anastomosis or Stump Leakage After Laparoscopic Gastrectomy: A Deep Learning Approach to Intraoperative Image Analysis.Journal of gastric cancer · 2025Article
- An effective image despeckling and reconstruction approach using U-Net based model and comparative analysis.Scientific reports · 2025Article
- DSTAN: A Deformable Spatial-temporal Attention Network with Bidirectional Sequence Feature Refinement for Speckle Noise Removal in Thyroid Ultrasound Video.Journal of imaging informatics in medicine · 2024Article
- Deep-learning radiomics based on ultrasound can objectively evaluate thyroid nodules and assist in improving the diagnostic level of ultrasound physicians.Quantitative imaging in medicine and surgery · 2024Article
- Deep learning prediction of renal anomalies for prenatal ultrasound diagnosis.Scientific reports · 2024Article
- Super-resolution of 2D ultrasound images and videos.Medical & biological engineering & computing · 2023Article
- Aided diagnosis of thyroid nodules based on an all-optical diffraction neural network.Quantitative imaging in medicine and surgery · 2023Article
- Real-time denoising of ultrasound images based on deep learning.Medical & biological engineering & computing · 2022Article
- Application and Optimization of Lee Filter for Segmentation of Benign Tumor in Breast Ultrasound Images.Journal of medical physicsArticle
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Authors and funding
3 authors.
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Abstract
Ultrasound images are widespread in medical diagnosis for muscle-skeletal, cardiac, and obstetrical diseases, due to the efficiency and non-invasiveness of the acquisition methodology. However, ultrasound acquisition introduces noise in the signal, which corrupts the resulting image and affects further processing steps, e.g. segmentation and quantitative analysis. We define a novel deep learning framework for the real-time denoising of ultrasound images. Firstly, we compare state-of-the-art methods for denoising (e.g. spectral, low-rank methods) and select WNNM (Weighted Nuclear Norm Minimisation) as the best denoising in terms of accuracy, preservation of anatomical features, and edge enhancement. Then, we propose a tuned version of WNNM (tuned-WNNM) that improves the quality of the denoised images and extends its applicability to ultrasound images. Through a deep learning framework, the tuned-WNNM qualitatively and quantitatively replicates WNNM results in real-time. Finally, our approach is general in terms of its building blocks and parameters of the deep learning and high-performance computing framework; in fact, we can select different denoising algorithms and deep learning architectures.
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