ArticleIEEE transactions on medical imaging2020
MimickNet, Mimicking Clinical Image Post- Processing Under Black-Box Constraints.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Ultrasound swallowing assessment: the impact of image post-processing on measurement reliability.Physical and engineering sciences in medicine · 2026Article
- On the public dissemination and open sourcing of ultrasound resources, datasets and deep learning models.NPJ digital medicine · 2025Article
- Migration of Deep Learning Models Across Ultrasound Scanners.IEEE transactions on bio-medical engineering · 2025Article
- Lumen segmentation using a Mask R-CNN in carotid arteries with stenotic atherosclerotic plaque.Ultrasonics · 2024Article
- A combined radiomic model distinguishing GISTs from leiomyomas and schwannomas in the stomach based on endoscopic ultrasonography images.Journal of applied clinical medical physics · 2023Article
- Diagnostic performance of deep learning-based vessel extraction and stenosis detection on coronary computed tomography angiography for coronary artery disease: a multi-reader multi-case study.La Radiologia medica · 2023Article
- Bibliometric analysis of 100 top cited articles of heart failure-associated diseases in combination with machine learning.Frontiers in cardiovascular medicine · 2023Article
- Medical ultrasound image speckle reduction and resolution enhancement using texture compensated multi-resolution convolution neural network.Frontiers in physiology · 2022Article
- Training Deep Network Ultrasound Beamformers With Unlabeled In Vivo Data.IEEE transactions on medical imaging · 2022Article
- Deep Convolutional Neural Networks for Displacement Estimation in ARFI Imaging.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2021Article
- Histogram Matching for Visual Ultrasound Image Comparison.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2021Article
- Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review.Journal of the American College of Cardiology · 2021Review
- Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency.Ultrasonography (Seoul, Korea) · 2021Article
- Cardiac point-of-care to cart-based ultrasound translation using constrained CycleGAN.International journal of computer assisted radiology and surgery · 2020Article
Corrections and comments
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Authors and funding
7 authors.
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Abstract
Image post-processing is used in clinical-grade ultrasound scanners to improve image quality (e.g., reduce speckle noise and enhance contrast). These post-processing techniques vary across manufacturers and are generally kept proprietary, which presents a challenge for researchers looking to match current clinical-grade workflows. We introduce a deep learning framework, MimickNet, that transforms conventional delay-and-summed (DAS) beams into the approximate Dynamic Tissue Contrast Enhanced (DTCE™) post-processed images found on Siemens clinical-grade scanners. Training MimickNet only requires post-processed image samples from a scanner of interest without the need for explicit pairing to DAS data. This flexibility allows MimickNet to hypothetically approximate any manufacturer's post-processing without access to the pre-processed data. MimickNet post-processing achieves a 0.940 ± 0.018 structural similarity index measurement (SSIM) compared to clinical-grade post-processing on a 400 cine-loop test set, 0.937 ± 0.025 SSIM on a prospectively acquired dataset, and 0.928 ± 0.003 SSIM on an out-of-distribution cardiac cine-loop after gain adjustment. To our knowledge, this is the first work to establish deep learning models that closely approximate ultrasound post-processing found in current medical practice. MimickNet serves as a clinical post-processing baseline for future works in ultrasound image formation to compare against. Additionally, it can be used as a pretrained model for fine-tuning towards different post-processing techniques. To this end, we have made the MimickNet software, phantom data, and permitted in vivo data open-source at https://github.com/ouwen/MimickNet.
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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.