Due to the limitations of sensors, the transmission medium and the intrinsic properties of ultrasound, the quality of ultrasound imaging is always not ideal, especially its low spatial resolution. To remedy this situation, deep learning networks have been recently developed for ultrasound image super-resolution (SR) because of the powerful approximation capability. However, most current supervised SR methods are not suitable for ultrasound medical images because the medical image samples are always rare, and usually, there are no low-resolution (LR) and high-resolution (HR) training pairs in reality. In this work, based on self-supervision and cycle generative adversarial network (CycleGAN), we propose a new perception consistency ultrasound image super-resolution (SR) method, which only requires the LR ultrasound data and can ensure the re-degenerated image of the generated SR one to be consistent with the original LR image, and vice versa. We first generate the HR fathers and the LR sons of the test ultrasound LR image through image enhancement, and then make full use of the cycle loss of LR-SR-LR and HR-LR-SR and the adversarial characteristics of the discriminator to promote the generator to produce better perceptually consistent SR results. The evaluation of PSNR/IFC/SSIM, inference efficiency and visual effects under the benchmark CCA-US and CCA-US datasets illustrate our proposed approach is effective and superior to other state-of-the-art methods.
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Samsung Medison Co., Ltd.（三星麦迪逊有限公司）；Chung-Ang University Hospital（中央大学医院）
In contrast to 2-D ultrasound (US) for uniaxial plane imaging, a 3-D US imaging system can visualize a volume along three axial planes. This allows for a full view of the anatomy, which is useful for gynecological (GYN) and obstetrical (OB) applications. Unfortunately, the 3-D US has an inherent limitation in resolution compared to the 2-D US. In the case of 3-D US with a 3-D mechanical probe, for example, the image quality is comparable along the beam direction, but significant deterioration in image quality is often observed in the other two axial image planes. To address this, here we propo…
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）
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…
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
Ultrasound (US) imaging is a fast and non-invasive imaging modality which is widely used for real-time clinical imaging applications without concerning about radiation hazard. Unfortunately, it often suffers from poor visual quality from various origins, such as speckle noises, blurring, multi-line acquisition (MLA), limited RF channels, small number of view angles for the case of plane wave imaging, etc. Classical methods to deal with these problems include image-domain signal processing approaches using various adaptive filtering and model-based approaches. Recently, deep learning approaches…
Multilevel Perception Boundary-guided Network for Breast Lesion Segmentation in Ultrasound Images
Guizhou University（贵州大学）；College of Computer Science and Technology, Guizhou University（贵州大学计算机科学与技术学院）；Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education（教育部文本计算与认知智能工程研究中心）；Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province（贵州省智能医学影像分析与精准诊断重点实验室）；State Key Laboratory of Public Big Data（公共大数据国家重点实验室）
Shenzhen University（深圳大学）；University of Leeds（利兹大学）；Shenzhen RayShape Medical Technology Co., Ltd（深圳睿影医疗科技有限公司）
Standard plane (SP) localization is essential in routine clinical ultrasound (US) diagnosis. Compared to 2D US, 3D US can acquire multiple view planes in one scan and provide complete anatomy with the addition of coronal plane. However, manually navigating SPs in 3D US is laborious and biased due to the orientation variability and huge search space. In this study, we introduce a novel reinforcement learning (RL) framework for automatic SP localization in 3D US. Our contribution is three-fold. First, we formulate SP localization in 3D US as a tangent-point-based problem in RL to restructure the…
UniUltra: Interactive Parameter-Efficient SAM2 for Universal Ultrasound Segmentation
School of Computer Science, University of Nottingham Ningbo China（Nottingham Ningbo中国大学计算机科学学院）；School of Computer Science, University of Nottingham, UK（Nottingham英国大学计算机科学学院）；School of Mathematical and Computational Sciences, Massey University（Massey大学数学与计算科学学院）；School of Data Science and Artificial Intelligence, Wenzhou University of Technology（温州科技大学数据科学与人工智能学院）；Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（香港科学院创新研究所）；The Hong Kong Polytechnic University（香港理工大学）
Shenzhen University（深圳大学）；Marshall Laboratory of Biomedical Engineering（马歇尔生物医学工程实验室）
3D ultrasound (US) is widely used for its rich diagnostic information. However, it is criticized for its limited field of view. 3D freehand US reconstruction is promising in addressing the problem by providing broad range and freeform scan. The existing deep learning based methods only focus on the basic cases of skill sequences, and the model relies on the training data heavily. The sequences in real clinical practice are a mix of diverse skills and have complex scanning paths. Besides, deep models should adapt themselves to the testing cases with prior knowledge for better robustness, rather…
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）
Clinical decision support using deep neural networks has become a topic of steadily growing interest. While recent work has repeatedly demonstrated that deep learning offers major advantages for medical image classification over traditional methods, clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. In recent years, this has been addressed by a variety of approaches that have successfully contributed to providing deeper insight. Most notably, additive feature attribution methods are…
CLADE: Cycle Loss Augmented Degradation Enhancement for Unpaired Super-Resolution of Anisotropic Medical Images
University College London（伦敦大学学院）；Institute of Cardiovascular Science（心血管科学研究所）；Centre for Medical Imaging（医学影像中心）；Frimley Health NHS Trust（弗林利健康 NHS 信托基金）；UCL NHS Trust（伦敦大学学院 NHS 信托基金）
