Ultrasound simulation based on ray tracing enables the synthesis of highly realistic images. It can provide an interactive environment for training sonographers as an educational tool. However, due to high computational demand, there is a trade-off between image quality and interactivity, potentially leading to sub-optimal results at interactive rates. In this work we introduce a deep learning approach based on adversarial training that mitigates this trade-off by improving the quality of simulated images with constant computation time. An image-to-image translation framework is utilized to translate low quality images into high quality versions. To incorporate anatomical information potentially lost in low quality images, we additionally provide segmentation maps to image translation. Furthermore, we propose to leverage information from acoustic attenuation maps to better preserve acoustic shadows and directional artifacts, an invaluable feature for ultrasound image interpretation. The proposed method yields an improvement of 7.2% in Fréchet Inception Distance and 8.9% in patch-based Kullback-Leibler divergence.
Ultrasound is the second most used modality in medical imaging. It is cost effective, hazardless, portable and implemented routinely in numerous clinical procedures. Nonetheless, image quality is characterized by granulated appearance, poor SNR and speckle noise. Specific for malignant tumors, the margins are blurred and indistinct. Thus, there is a great need for improving ultrasound image quality. We hypothesize that this can be achieved, using neural networks, by translation into a more realistic display which mimics an anatomical cut through the tissue. In order to achieve this goal, the p…
Influence of High-Performance Image-to-Image Translation Networks on Clinical Visual Assessment and Outcome Prediction: Utilizing Ultrasound to MRI Translation in Prostate Cancer
UltraDfeGAN: Detail-Enhancing Generative Adversarial Networks for High-Fidelity Functional Ultrasound Synthesis
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Department of Information and Communication, Institute of Technology of Cambodia（柬埔寨技术学院信息与通信系）；Department of Telecommunication and Network Engineering, Institute of Technology of Cambodia（柬埔寨技术学院电信与网络工程系）
Computational simulation of ultrasound (US) echography is essential for training sonographers. Realistic simulation of US interaction with microscopic tissue structures is often modeled by a tissue representation in the form of point scatterers, convolved with a spatially varying point spread function. This yields a realistic US B-mode speckle texture, given that a scatterer representation for a particular tissue type is readily available. This is often not the case and scatterers are nontrivial to determine. In this work we propose to estimate scatterer maps from sample US B-mode images of a …
Interactive simulation of ultrasound imaging greatly facilitates sonography training. Although ray-tracing based methods have shown promising results, obtaining realistic images requires substantial modeling effort and manual parameter tuning. In addition, current techniques still result in a significant appearance gap between simulated images and real clinical scans. Herein we introduce a novel content-preserving image translation framework (ConPres) to bridge this appearance gap, while maintaining the simulated anatomical layout. We achieve this goal by leveraging both simulated images with …
Photorealistic image generation from simulated label maps are necessitated in several contexts, such as for medical training in virtual reality. With conventional deep learning methods, this task requires images that are paired with semantic annotations, which typically are unavailable. We introduce a contrastive learning framework for generating photorealistic images from simulated label maps, by learning from unpaired sets of both. Due to potentially large scene differences between real images and label maps, existing unpaired image translation methods lead to artifacts of scene modification…
CSG: A Context-Semantic Guided Diffusion Approach in De Novo Musculoskeletal Ultrasound Image Generation
GE Healthcare（GE医疗）；Science & Technology Organization, GE Healthcare（GE医疗科技组织）；Point of Care Ultrasound & Handheld（床旁超声与 handheld（注：此处保留原文表述，结合上下文为GE医疗相关临床应用部门，按要求提取到机构级别））
Domain Adaptation of Carotid Ultrasound Images using Generative Adversarial Network
Department of Diagnostics and Intervention, and Biomedical Engineering, Umea University（诊断与介入系及生物医学工程系，乌梅亚大学）；Department of Computer Science and Information Engineering, National Taipei University of Technology（计算机科学与信息工程系，国家台北科技大学）；IRC for Bio Systems and Machines, King Fahd University of Petroleum and Minerals（生物系统与机器研究院，国王法赫德石油与矿物大学）
Shanghai Jiao Tong University（上海交通大学）；Shanghai General Hospital（上海市第一人民医院）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）；AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院）
Much progress has been made in the deep neural network (DNN) based diagnosis of mass lesions breast ultrasound (BUS) images. However, the non-mass lesion is less investigated because of the limited data. Based on the insight that mass data is sufficient and shares the same knowledge structure with non-mass data of identifying the malignancy of a lesion based on the ultrasound image, we propose a novel transfer learning framework to enhance the generalizability of the DNN model for non-mass BUS with the help of mass BUS. Specifically, we train a shared DNN with combined non-mass and mass data. …
Generative deep learning for foundational video translation in ultrasound
Department of Medicine, Division of Cardiology（医学系，心脏病科）；Bakar Computational Health Sciences Institute（Bakar计算健康科学研究所）；University of California, San Francisco（加州大学旧金山分校）；UCSF-UC Berkeley Joint Program in Computational Precision Health（UCSF-伯克利计算精准健康联合项目）；Department of Radiology, Center for Intelligent Imaging（放射科，智能成像中心）
Purpose. Given the high level of expertise required for navigation and interpretation of ultrasound images, computational simulations can facilitate the training of such skills in virtual reality. With ray-tracing based simulations, realistic ultrasound images can be generated. However, due to computational constraints for interactivity, image quality typically needs to be compromised. Methods. We propose herein to bypass any rendering and simulation process at interactive time, by conducting such simulations during a non-time-critical offline stage and then learning image translation from cro…
Ultrasound (US) imaging is one of the most commonly used non-invasive imaging techniques. However, US image acquisition requires simultaneous guidance of the transducer and interpretation of images, which is a highly challenging task that requires years of training. Despite many recent developments in intra-examination US image analysis, the results are not easy to translate to a clinical setting. We propose a generic framework to extract the US images and superimpose the results of an analysis task, without any need for physical connection or alteration to the US system. The proposed method c…
CycleULM: A unified label-free deep learning framework for ultrasound localisation microscopy
Department of Bioengineering, Faculty of Engineering, Imperial College London（生物工程系，工程学院，帝国理工学院伦敦分校）；Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London（电气与电子工程系，工程学院，帝国理工学院伦敦分校）；Faculty of Electrical and Computer Engineering, Escuela Superior Politécnica del Litoral (ESPOL)（电气与计算机工程学院，ESPOL高级理工大学）
Semantic Scene Graph for Ultrasound Image Explanation and Scanning Guidance
Computer Aided Medical Procedures (CAMP)（计算机辅助医疗程序）；TU Munich, Germany（慕尼黑工业大学，德国）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；The Chinese University of Hong Kong, Hong Kong, China（香港中文大学，中国香港）
Hybrid Attention Network for Accurate Breast Tumor Segmentation in Ultrasound Images
School of Information Engineering, Xi’an Eurasia University（信息工程学院，西安欧亚大学）；Department of Computer Science, University of Engineering and Technology Lahore（计算机科学系，拉合尔工程科技大学）
A Paired Point-of-Care Ultrasound Dataset for Image Quality Enhancement and Benchmarking via a cGAN Baseline
Department of Nanobiophysics, Faculty of Science and Technology, University of Twente（特文特大学科学与技术学院生物医学系）；Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute（荷兰癌症研究所手术引导外科部）；Netherlands Cancer Institute（荷兰癌症研究所）；Center for Early Cancer Detection, Netherlands Cancer Institute（荷兰癌症研究所早期癌症检测中心）
PADReg: Physics-Aware Deformable Registration Guided by Contact Force for Ultrasound Sequences
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Center for Artificial Intelligence and Robotics, HK Institute of Science & Innovation, Chinese Academy of Sciences（人工智能与机器人中心，香港创新科学院，中国科学院）；School of Biomedical Engineering and Imaging Sciences, King’s College London（生物医学工程与影像科学学院，伦敦国王学院）
Breast Ultrasound Tumor Generation via Mask Generator and Text-Guided Network:A Clinically Controllable Framework with Downstream Evaluation
School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China（生物医学工程学院，深圳大学医学院，深圳大学，深圳，中国）；Smart Medical Imaging, Learning and Engineering (SMILE) Lab, Shenzhen University, Shenzhen, China（智能医学影像、学习与工程（SMILE）实验室，深圳大学，深圳，中国）；Nuclear Medicine Department, the Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China（核医学科，中山大学第七附属医院，中山大学，深圳，中国）；Department of Radiology, the Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China（放射科，中山大学第七附属医院，中山大学，深圳，中国）；Shenzhen University of Advanced Technology, Shenzhen, China（深圳大学先进技术大学，深圳，中国）
In this paper we present a new method for motion tracking of tumors in liver ultrasound image sequences. Our algorithm has two main steps. In the first step, we apply mean shift algorithm with multiple features to estimate the center of the target in each frame. Target in the first frame is defined using an ellipse. Edge, texture, and intensity features are extracted from the first frame, and then mean shift algorithm is applied to each feature separately to find the center of ellipse related to that feature in the next frame. The center of ellipse will be the weighted average of these centers…
TRUST: Efficient Abdominal Trauma Recognition via Image-to-Ultrasound-Video Transfer Learning
School of Biological Science and Medical Engineering, Southeast University（东南大学生物科学与医学工程学院）；Nanjing Medical University First Affiliated Hospital（南京医科大学第一附属医院）；Jiangsu Key Laboratory of Biomaterials and Devices, Southeast University（江苏省生物材料与器件重点实验室，东南大学）；State Key Laboratory of Digital Medical Engineering, Southeast University（数字医学工程国家重点实验室，东南大学）
Enhancing Image Restoration Transformer via Adaptive Translation Equivariance
Institute of Medical Technology, Peking University Health Science Center, Peking University（北京大学医学部医学技术研究所）；Biomedical Engineering Department, College of Future Technology, Peking University（未来技术学院生物医学工程系）；National Biomedical Imaging Center, Peking University（北京大学国家生物医学成像中心）；China Mobile Research Institute（中国移动研究院）
3DFETUS: Deep Learning-Based Standardization of Facial Planes in 3D Ultrasound
Department of Engineering, Universitat Pompeu Fabra（工程系，庞培法布拉大学）；Department of Obstetrics and Gynecology, Hospital del Mar（妇产科，马尔医院）；Department of Robotics, Universitat Politecnica de Catalunya（机器人学系，加泰罗尼亚理工大学）；Gynecology and Reproductive Medicine, University Hospital Quirón Dexeus（妇产科与生殖医学，Quirón Dexeus大学医院）
A Unified Platform and Quality Assurance Framework for 3D Ultrasound Reconstruction with Robotic, Optical, and Electromagnetic Tracking
School of Computer Science, University of Leeds（利兹大学计算机科学学院）；School of Electronic and Electrical Engineering, University of Leeds（利兹大学电子与电气工程学院）；St James’s University Hospital, Leeds Teaching Hospitals NHS Trust（利兹教学医院，利兹教学医院国家健康服务信托基金）；Leeds Institute of Medical Research, University of Leeds（利兹医学研究所，利兹大学）
Vascular Segmentation of Functional Ultrasound Images using Deep Learning
AIstroSight；Inria（法国国家信息与自动化技术研究院）；Hospices Civils de Lyon（里昂慈善医院）；University Claude Bernard Lyon 1（里昂第一大学）；Lyon Neuroscience Research Center（里昂神经科学研究中心）
