3D ultrasound (US) can facilitate detailed prenatal examinations for fetal growth monitoring. To analyze a 3D US volume, it is fundamental to identify anatomical landmarks of the evaluated organs accurately. Typical deep learning methods usually regress the coordinates directly or involve heatmap-matching. However, these methods struggle to deal with volumes with large sizes and the highly-varying positions and orientations of fetuses. In this work, we exploit an object detection framework to detect landmarks in 3D fetal facial US volumes. By regressing multiple parameters of the landmark-centered bounding box (B-box) with a strict criteria, the proposed model is able to pinpoint the exact location of the targeted landmarks. Specifically, the model uses a 3D region proposal network (RPN) to generate 3D candidate regions, followed by several 3D classification branches to select the best candidate. It also adopts an IoU-balance loss to improve communications between branches that benefits the learning process. Furthermore, it leverages a distance-based graph prior to regularize the training and helps to reduce false positive predictions. The performance of the proposed framework is evaluated on a 3D US dataset to detect five key fetal facial landmarks. Results showed the proposed method outperforms some of the state-of-the-art methods in efficacy and efficiency.
Shenzhen University（深圳大学）；Nanfang Medical University（南方医科大学）；The Chinese University of Hong Kong（香港中文大学）
Volumetric ultrasound has great potentials in promoting prenatal examinations. Automated solutions are highly desired to efficiently and effectively analyze the massive volumes. Segmentation and landmark localization are two key techniques in making the quantitative evaluation of prenatal ultrasound volumes available in clinic. However, both tasks are non-trivial when considering the poor image quality, boundary ambiguity and anatomical variations in volumetric ultrasound. In this paper, we propose an effective framework for simultaneous segmentation and landmark localization in prenatal ultra…
University of Chinese Academy of Sciences（中国科学院大学）；Huawei Inc（华为公司）；Huazhong University of Science and Technology（华中科技大学）
Object detection, instance segmentation, and pose estimation are popular visual recognition tasks which require localizing the object by internal or boundary landmarks. This paper summarizes these tasks as location-sensitive visual recognition and proposes a unified solution named location-sensitive network (LSNet). Based on a deep neural network as the backbone, LSNet predicts an anchor point and a set of landmarks which together define the shape of the target object. The key to optimizing the LSNet lies in the ability of fitting various scales, for which we design a novel loss function named…
One usage of medical ultrasound imaging is to visualize and characterize human tongue shape and motion during a real-time speech to study healthy or impaired speech production. Due to the low-contrast characteristic and noisy nature of ultrasound images, it might require expertise for non-expert users to recognize tongue gestures in applications such as visual training of a second language. Moreover, quantitative analysis of tongue motion needs the tongue dorsum contour to be extracted, tracked, and visualized. Manual tongue contour extraction is a cumbersome, subjective, and error-prone task.…
Università degli studi di Milano（米兰大学）；Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico di Milano（米兰马焦雷医院格兰达卡基金会IRCCS）
Joint bleeding is a common condition for people with hemophilia and, if untreated, can result in hemophilic arthropathy. Ultrasound imaging has recently emerged as an effective tool to diagnose joint recess distension caused by joint bleeding. However, no computer-aided diagnosis tool exists to support the practitioner in the diagnosis process. This paper addresses the problem of automatically detecting the recess and assessing whether it is distended in knee ultrasound images collected in patients with hemophilia. After framing the problem, we propose two different approaches: the first one a…
Robot-guided catheter insertion has the potential to deliver urgent medical care in situations where medical personnel are unavailable. However, this technique requires accurate and reliable segmentation of anatomical landmarks in the body. For the ultrasound imaging modality, obtaining large amounts of training data for a segmentation model is time-consuming and expensive. This paper introduces RESUS (RESlicing of UltraSound Images), a weak supervision data augmentation technique for ultrasound images based on slicing reconstructed 3D volumes from tracked 2D images. This technique allows us t…
Real-Time Brain Tumor Detection in Intraoperative Ultrasound Using YOLO11: From Model Training to Deployment in the Operating Room
Department of Neurosurgery, Rio Hortega University Hospital（里奥霍尔特加大学医院神经外科部）；Department of Neurosurgery, Tata Memorial Centre, Homi Bhabha National Institute（塔塔纪念中心神经外科部，霍米·巴布哈国家研究所）；Biomedical Engineering Group, University of Valladolid（瓦拉多利德大学生物医学工程小组）；UMR 1253, iBrain, Université de Tours, Inserm（UMR 1253，iBrain， Tours大学，Inserm）；Department of Neurosurgery, CHRU de Tours（图尔CHRU医院神经外科部）；Department of Neurosrugery, ARNAS Civico Di Cristina Benfratelli Hospital（ARNAS公民迪克里斯塔弗拉蒂医院神经外科部）；Fondazione IRCCS Istituto Neurologico Carlo Besta（IRCCS神经学研究所卡尔罗·贝斯塔基金会）；University of Milan（米兰大学）；Johns Hopkins Medical School（约翰霍普金斯医学院）
Sagol Brain Institute, Tel Aviv Sourasky Medical Center（特拉维夫苏拉斯基医疗中心萨戈尔脑研究所）；University College London（伦敦大学学院）
Fetal growth assessment from ultrasound is based on a few biometric measurements that are performed manually and assessed relative to the expected gestational age. Reliable biometry estimation depends on the precise detection of landmarks in standard ultrasound planes. Manual annotation can be time-consuming and operator dependent task, and may results in high measurements variability. Existing methods for automatic fetal biometry rely on initial automatic fetal structure segmentation followed by geometric landmark detection. However, segmentation annotations are time-consuming and may be inac…
Utah State University（犹他州立大学）；Harbin Institute of Technology（哈尔滨工业大学）；the First Affiliated Hospital of Harbin Medical University（哈尔滨医科大学附属第一医院）
Breast cancer is one of the most serious disease affecting women's health. Due to low cost, portable, no radiation, and high efficiency, breast ultrasound (BUS) imaging is the most popular approach for diagnosing early breast cancer. However, ultrasound images are low resolution and poor quality. Thus, developing accurate detection system is a challenging task. In this paper, we propose a fully automatic segmentation algorithm consisting of two parts: fuzzy fully convolutional network and accurately fine-tuning post-processing based on breast anatomy constraints. In the first part, the image i…
Rethinking Medical Landmark Localization with Prototype Learning-based Progressive Offset Correction
Shenzhen University（深圳大学）；Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）；School of Biomedical Engineering and Informatics, Nanjing Medical University（南京医科大学生物医学工程与信息学院）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University（深圳大学大数据系统计算技术国家工程实验室）
CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network
School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；The Affiliated Hospital of Qingdao University（青岛大学附属医院）；University of Idaho（爱达荷大学）
University of Leicester（莱斯特大学）；University Hospitals of Leicester NHS Trust（莱斯特大学医院NHS信托）；Affiliated Hospital of Nantong University（南通大学附属医院）
Accurate tracking of an anatomical landmark over time has been of high interests for disease assessment such as minimally invasive surgery and tumor radiation therapy. Ultrasound imaging is a promising modality benefiting from low-cost and real-time acquisition. However, generating a precise landmark tracklet is very challenging, as attempts can be easily distorted by different interference such as landmark deformation, visual ambiguity and partial observation. In this paper, we propose a long-short diffeomorphic motion network, which is a multi-task framework with a learnable deformation prio…
Siemens Healthineers（西门子医疗）；Imperial College London（帝国理工学院）
Real-time localization of prostate gland in trans-rectal ultrasound images is a key technology that is required to automate the ultrasound guided prostate biopsy procedures. In this paper, we propose a new deep learning based approach which is aimed at localizing several prostate landmarks efficiently and robustly. We propose a multitask learning approach primarily to make the overall algorithm more contextually aware. In this approach, we not only consider the explicit learning of landmark locations, but also build-in a mechanism to learn the contour of the prostate. This multitask learning i…
Siemens Healthineers（西门子医疗）；Vanderbilt University（范德堡大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
An abdominal ultrasound examination, which is the most common ultrasound examination, requires substantial manual efforts to acquire standard abdominal organ views, annotate the views in texts, and record clinically relevant organ measurements. Hence, automatic view classification and landmark detection of the organs can be instrumental to streamline the examination workflow. However, this is a challenging problem given not only the inherent difficulties from the ultrasound modality, e.g., low contrast and large variations, but also the heterogeneity across tasks, i.e., one classification task…
T2ID-CAS: Diffusion Model and Class Aware Sampling to Mitigate Class Imbalance in Neck Ultrasound Anatomical Landmark Detection
Department of Computer Science, University of Central Florida（计算机科学系，中央佛罗里达大学）；Department of Otolaryngology Head Neck Surgery, University of Cincinnati College of Medicine（耳鼻喉科与头颈外科系，辛辛那提大学医学院）；Department of Internal Medicine, University of Central Florida College of Medicine（内科系，中央佛罗里达大学医学院）
Image-guided radiation therapy can benefit from accurate motion tracking by ultrasound imaging, in order to minimize treatment margins and radiate moving anatomical targets, e.g., due to breathing. One way to formulate this tracking problem is the automatic localization of given tracked anatomical landmarks throughout a temporal ultrasound sequence. For this, we herein propose a fully-convolutional Siamese network that learns the similarity between pairs of image regions containing the same landmark. Accordingly, it learns to localize and thus track arbitrary image features, not only predefine…
Inspired by the recent development of deep network-based methods in semantic image segmentation, we introduce an end-to-end trainable model for face mask extraction in video sequence. Comparing to landmark-based sparse face shape representation, our method can produce the segmentation masks of individual facial components, which can better reflect their detailed shape variations. By integrating Convolutional LSTM (ConvLSTM) algorithm with Fully Convolutional Networks (FCN), our new ConvLSTM-FCN model works on a per-sequence basis and takes advantage of the temporal correlation in video clips. …
Indian Institute of Technology Palakkad（帕拉卡德印度理工学院）；La Paz Hospital（拉巴斯医院）；Government Medical College Thiruvananthapuram（特里凡得琅医学院）
Lung ultrasound (LUS) is an increasingly popular diagnostic imaging modality for continuous and periodic monitoring of lung infection, given its advantages of non-invasiveness, non-ionizing nature, portability and easy disinfection. The major landmarks assessed by clinicians for triaging using LUS are pleura, A and B lines. There have been many efforts for the automatic detection of these landmarks. However, restricting to a few pre-defined landmarks may not reveal the actual imaging biomarkers particularly in case of new pathologies like COVID-19. Rather, the identification of key landmarks s…
Measurement of Medial Elbow Joint Space using Landmark Detection
Graduate School of Science and Technology, University of Tsukuba（科学技术研究生院，茨木大学）；Department of Orthopaedic Surgery, University of Tsukuba（骨科手术部，茨木大学）；Department of Orthopaedic Surgery, Ibaraki Medical Center, Tokyo Medical University（Ibaraki医疗中心，东京医学大学骨科手术部）；Institute of Systems and Information Engineering, University of Tsukuba（系统与信息工程研究所，茨木大学）；Department of Orthopaedic Surgery, Kikkoman General Hospital（Kikkoman总医院骨科手术部）；Center for Computational Sciences, University of Tsukuba（计算科学中心，茨木大学）；Center for Cyber Medicine Research, University of Tsukuba（网络医学研究室，茨木大学）
Imperial College London（帝国理工学院）；Samsung AI Centre（三星人工智能中心）
Semantic segmentation of eyes has long been a vital pre-processing step in many biometric applications. Majority of the works focus only on high resolution eye images, while little has been done to segment the eyes from low quality images in the wild. However, this is a particularly interesting and meaningful topic, as eyes play a crucial role in conveying the emotional state and mental well-being of a person. In this work, we take two steps toward solving this problem: (1) We collect and annotate a challenging eye segmentation dataset containing 8882 eye patches from 4461 facial images of dif…
Johns Hopkins University（约翰斯·霍普金斯大学）；Rutgers, The State University of New Jersey（罗格斯大学，新泽西州立大学）
Due to its excellent performance, U-Net is the most widely used backbone architecture for biomedical image segmentation in the recent years. However, in our studies, we observe that there is a considerable performance drop in the case of detecting smaller anatomical landmarks with blurred noisy boundaries. We analyze this issue in detail, and address it by proposing an over-complete architecture (Ki-Net) which involves projecting the data onto higher dimensions (in the spatial sense). This network, when augmented with U-Net, results in significant improvements in the case of segmenting small a…
Cochin University of Science and Technology（科钦科技大学）；Indian Institute of Technology Palakkad（帕拉卡德印度理工学院）
In the recent past with the rapid surge of COVID-19 infections, lung ultrasound has emerged as a fast and powerful diagnostic tool particularly for continuous and periodic monitoring of the lung. There have been many attempts towards severity classification, segmentation and detection of key landmarks in the lung. Leveraging the progress, an automated lung ultrasound video analysis package is presented in this work, which can provide summary of key frames in the video, flagging of the key frames with lung infection and options to automatically detect and segment the lung landmarks. The integra…
The University of Tokyo（东京大学）；Computer Science and Artificial Intelligence Laboratory, MIT（麻省理工学院计算机科学与人工智能实验室）；Brigham and Women’s Hospital, Harvard Medical School（哈佛医学院布莱根妇女医院）；Ecole de Technologie Superieure（高等技术学院）；Lehigh University（里海大学）；RIKEN（理化学研究所）
A reliable Ultrasound (US)-to-US registration method to compensate for brain shift would substantially improve Image-Guided Neurological Surgery. Developing such a registration method is very challenging, due to factors such as missing correspondence in images, the complexity of brain pathology and the demand for fast computation. We propose a novel feature-driven active framework. Here, landmarks and their displacement are first estimated from a pair of US images using corresponding local image features. Subsequently, a Gaussian Process (GP) model is used to interpolate a dense deformation fi…
Image guidance has been widely used in radiation therapy. Correctly identifying the bounding box of the anatomical landmarks from limited field of views is the key to success. In image-guided radiation therapy (IGRT), the detection of those landmarks like the 12th vertebra (T12) still requires tedious manual inspections and annotations; and superior-inferior misalignment to the wrong vertebral body is still relatively common. It is necessary to develop an automated approach to detect those landmarks from images. The challenges of training a model to identify T12 vertebrae automatically mainly …
Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔兰根-纽伦堡大学）；Siemens Healthcare GmbH（西门子医疗保健有限公司）；Hochschule Furtwangen（富特旺根应用技术大学）；BG Trauma Center Ludwigshafen（路德维希港BG创伤中心）
X-ray based measurement and guidance are commonly used tools in orthopaedic surgery to facilitate a minimally invasive workflow. Typically, a surgical planning is first performed using knowledge of bone morphology and anatomical landmarks. Information about bone location then serves as a prior for registration during overlay of the planning on intra-operative X-ray images. Performing these steps manually however is prone to intra-rater/inter-rater variability and increases task complexity for the surgeon. To remedy these issues, we propose an automatic framework for planning and subsequent ove…
Automatic Ultrasound Curve Angle Measurement via Affinity Clustering for Adolescent Idiopathic Scoliosis Evaluation
The Hong Kong Polytechnic University（香港理工大学）；The Chinese University of Hong Kong（香港中文大学）；Joint Scoliosis Research Center of the Chinese University of Hong Kong and Nanjing University（香港中文大学与南京大学联合脊柱侧弯研究中心）；Nanjing University（南京大学）
The University of British Columbia（不列颠哥伦比亚大学）；Vancouver General Hospital（温哥华综合医院）
This paper presents U-LanD, a framework for joint detection of key frames and landmarks in videos. We tackle a specifically challenging problem, where training labels are noisy and highly sparse. U-LanD builds upon a pivotal observation: a deep Bayesian landmark detector solely trained on key video frames, has significantly lower predictive uncertainty on those frames vs. other frames in videos. We use this observation as an unsupervised signal to automatically recognize key frames on which we detect landmarks. As a test-bed for our framework, we use ultrasound imaging videos of the heart, whe…
FetusMapV2: Enhanced Fetal Pose Estimation in 3D Ultrasound
Shenzhen University（深圳大学）；Shenzhen RayShape Medical Technology Co., Ltd（深圳睿心医疗科技有限公司）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）
Accurate detection of anatomical landmarks is an essential step in several medical imaging tasks. We propose a novel communicative multi-agent reinforcement learning (C-MARL) system to automatically detect landmarks in 3D brain images. C-MARL enables the agents to learn explicit communication channels, as well as implicit communication signals by sharing certain weights of the architecture among all the agents. The proposed approach is evaluated on two brain imaging datasets from adult magnetic resonance imaging (MRI) and fetal ultrasound scans. Our experiments show that involving multiple coo…
Anatomical landmarks are a crucial prerequisite for many medical imaging tasks. Usually, the set of landmarks for a given task is predefined by experts. The landmark locations for a given image are then annotated manually or via machine learning methods trained on manual annotations. In this paper, in contrast, we present a method to automatically discover and localize anatomical landmarks in medical images. Specifically, we consider landmarks that attract the visual attention of humans, which we term visually salient landmarks. We illustrate the method for fetal neurosonographic images. First…
Centre for Medical Image Computing, University College London（伦敦大学学院医学图像计算中心）；Institute of Biomedical Engineering, University of Oxford（牛津大学生物医学工程研究所）；Department of Diagnostic Imaging, Royal Free London NHS Foundation Trust（伦敦皇家自由NHS基金会信托诊断影像科）
Sonography synthesis has a wide range of applications, including medical procedure simulation, clinical training and multimodality image registration. In this paper, we propose a machine learning approach to simulate ultrasound images at given 3D spatial locations (relative to the patient anatomy), based on conditional generative adversarial networks (GANs). In particular, we introduce a novel neural network architecture that can sample anatomically accurate images conditionally on spatial position of the (real or mock) freehand ultrasound probe. To ensure an effective and efficient spatial in…
Identifying landmarks in the femoral area is crucial for ultrasound (US) -based robot-guided catheter insertion, and their presentation varies when imaged with different scanners. As such, the performance of past deep learning-based approaches is also narrowly limited to the training data distribution; this can be circumvented by fine-tuning all or part of the model, yet the effects of fine-tuning are seldom discussed. In this work, we study the US-based segmentation of multiple classes through transfer learning by fine-tuning different contiguous blocks within the model, and evaluating on a g…
Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London（伦敦大学学院威康/EPSRC介入与外科学中心）；UCL Centre for Medical Image Computing, University College London（伦敦大学学院医学图像计算中心）；Department of Gastroenterology, University College London Hospital（伦敦大学学院医院消化内科）；Division of Surgery and Interventional Science, University College London（伦敦大学学院外科与介入科学部）；Institute for Liver and Digestive Health, University College London（伦敦大学学院肝脏与消化健康研究所）
Ultrasound imaging is a commonly used technology for visualising patient anatomy in real-time during diagnostic and therapeutic procedures. High operator dependency and low reproducibility make ultrasound imaging and interpretation challenging with a steep learning curve. Automatic image classification using deep learning has the potential to overcome some of these challenges by supporting ultrasound training in novices, as well as aiding ultrasound image interpretation in patient with complex pathology for more experienced practitioners. However, the use of deep learning methods requires a la…
Imperial College London（伦敦帝国理工学院）；King’s College London（伦敦国王学院）
We propose a new Patch-based Iterative Network (PIN) for fast and accurate landmark localisation in 3D medical volumes. PIN utilises a Convolutional Neural Network (CNN) to learn the spatial relationship between an image patch and anatomical landmark positions. During inference, patches are repeatedly passed to the CNN until the estimated landmark position converges to the true landmark location. PIN is computationally efficient since the inference stage only selectively samples a small number of patches in an iterative fashion rather than a dense sampling at every location in the volume. Our …
Indian Institute of Technology Palakkad（印度理工学院帕拉卡德分校）；Hospital Universitario Puerta de Hierro（普埃尔塔·德·耶罗大学医院）；Sree Chitra Tirunal Institute for Medical Sciences & Technology（斯里·奇特拉·蒂鲁纳尔医学科学与技术研究所）；Government Medical College Thiruvananthapuram（特里凡得琅政府医学院）
Lung ultrasound (LUS) is possibly the only medical imaging modality which could be used for continuous and periodic monitoring of the lung. This is extremely useful in tracking the lung manifestations either during the onset of lung infection or to track the effect of vaccination on lung as in pandemics such as COVID-19. There have been many attempts in automating the classification of severity of lung into various classes or automatic segmentation of various LUS landmarks and manifestations. However, all these approaches are based on training static machine learning models which require a sig…
Sequential Spatial-Temporal Network for Interpretable Automatic Ultrasonic Assessment of Fetal Head during labor
School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；Sydney School of Health Sciences, The University of Sydney（悉尼大学悉尼健康科学学院）；School of Science, Edith Cowan University（伊迪丝·考恩大学理学院）；Orange Hospital（奥兰治医院）
Southern University of Science and Technology（南方科技大学）；The Chinese University of Hong Kong（香港中文大学）；Air Force Medical Center, PLA（中国人民解放军空军特色医学中心）
Colonoscopy is a standard imaging tool for visualizing the entire gastrointestinal (GI) tract of patients to capture lesion areas. However, it takes the clinicians excessive time to review a large number of images extracted from colonoscopy videos. Thus, automatic detection of biological anatomical landmarks within the colon is highly demanded, which can help reduce the burden of clinicians by providing guidance information for the locations of lesion areas. In this article, we propose a novel deep learning-based approach to detect biological anatomical landmarks in colonoscopy videos. First, …
Automatic and accurate detection of anatomical landmarks is an essential operation in medical image analysis with a multitude of applications. Recent deep learning methods have improved results by directly encoding the appearance of the captured anatomy with the likelihood maps (i.e., heatmaps). However, most current solutions overlook another essence of heatmap regression, the objective metric for regressing target heatmaps and rely on hand-crafted heuristics to set the target precision, thus being usually cumbersome and task-specific. In this paper, we propose a novel learning-to-learn frame…
Shenzhen University（深圳大学）；Guangdong Province Traditional Chinese Medical Hospital（广东省中医院）
Automated breast ultrasound (ABUS) is a new and promising imaging modality for breast cancer detection and diagnosis, which could provide intuitive 3D information and coronal plane information with great diagnostic value. However, manually screening and diagnosing tumors from ABUS images is very time-consuming and overlooks of abnormalities may happen. In this study, we propose a novel two-stage 3D detection network for locating suspected lesion areas and further classifying lesions as benign or malignant tumors. Specifically, we propose a 3D detection network rather than frequently-used segme…
University College London（伦敦大学学院）；Elizabeth Garrett Anderson Institute for Women’s Health, University College London（伦敦大学学院伊丽莎白·加勒特·安德森妇女健康研究所）；NIHR University College London Hospitals Biomedical Research Centre（英国国家健康研究所伦敦大学学院医院生物医学研究中心）
During pregnancy, ultrasound examination in the second trimester can assess fetal size according to standardized charts. To achieve a reproducible and accurate measurement, a sonographer needs to identify three standard 2D planes of the fetal anatomy (head, abdomen, femur) and manually mark the key anatomical landmarks on the image for accurate biometry and fetal weight estimation. This can be a time-consuming operator-dependent task, especially for a trainee sonographer. Computer-assisted techniques can help in automating the fetal biometry computation process. In this paper, we present a uni…
Universitat Rovira i Virgili（罗维拉-威尔吉利大学）；Bioinformatics Institute（生物信息学研究所）
This paper proposes an efficient solution for tumor segmentation and classification in breast ultrasound (BUS) images. We propose to add an atrous convolution layer to the conditional generative adversarial network (cGAN) segmentation model to learn tumor features at different resolutions of BUS images. To automatically re-balance the relative impact of each of the highest level encoded features, we also propose to add a channel-wise weighting block in the network. In addition, the SSIM and L1-norm loss with the typical adversarial loss are used as a loss function to train the model. Our model…
With superiorities on low cost, portability, and free of radiation, echocardiogram is a widely used imaging modality for left ventricle (LV) function quantification. However, automatic LV segmentation and motion tracking is still a challenging task. In addition to fuzzy border definition, low contrast, and abounding artifacts on typical ultrasound images, the shape and size of the LV change significantly in a cardiac cycle. In this work, we propose a temporal affine network (TAN) to perform image analysis in a warped image space, where the shape and size variations due to the cardiac motion as…
Beihang University（北京航空航天大学）；XuanWu Hospital, Capital Medical University（首都医科大学宣武医院）；Beijing Diagnostic Center of Vascular Ultrasound（北京血管超声诊断中心）
Carotid arteries vulnerable plaques are a crucial factor in the screening of atherosclerosis by ultrasound technique. However, the plaques are contaminated by various noises such as artifact, speckle noise, and manual segmentation may be time-consuming. This paper proposes an automatic convolutional neural network (CNN) method for plaque segmentation in carotid ultrasound images using a small dataset. First, a parallel network with three independent scale decoders is utilized as our base segmentation network, pyramid dilation convolutions are used to enlarge receptive fields in the three segme…
Depth-Driven Geometric Prompt Learning for Laparoscopic Liver Landmark Detection
The Chinese University of Hong Kong（香港中文大学）；The Hong Kong Polytechnic University（香港理工大学）；Shenzhen Institute of Advanced Technology, CAS（中国科学院深圳先进技术研究院）
Beijing Institute of Technology（北京理工大学）；The First Affiliated Hospital of Jinzhou Medical University（锦州医科大学附属第一医院）；Zhejiang University of Science & Technology（浙江科技学院）；Peking University People’s Hospital（北京大学人民医院）
Calcaneus is the largest tarsal bone to withstand the daily stresses of weight-bearing. The calcaneal fracture is the most common type in the tarsal bone fractures. After a fracture is suspected, plain radiographs should be taken first. Bohler's Angle (BA) and Critical Angle of Gissane (CAG), measured by four anatomic landmarks in lateral foot radiograph, can guide fracture diagnosis and facilitate operative recovery of the fractured calcaneus. This study aims to develop an analysis system that can automatically locate four anatomic landmarks, measure BA and CAG for fracture assessment, identi…
Emory University（埃默里大学）；University of Chicago（芝加哥大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University School of Medicine（埃默里大学医学院）
This study proposed a deep learning-based tracking method for ultrasound (US) image-guided radiation therapy. The proposed cascade deep learning model is composed of an attention network, a mask region-based convolutional neural network (mask R-CNN), and a long short-term memory (LSTM) network. The attention network learns a mapping from a US image to a suspected area of landmark motion in order to reduce the search region. The mask R-CNN then produces multiple region-of-interest (ROI) proposals in the reduced region and identifies the proposed landmark via three network heads: bounding box re…
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）
GE Vingmed Ultrasound, GE Healthcare（通用电气医疗集团GE Vingmed超声部门）；University of Oslo（奥斯陆大学）；Norwegian Computing Center（挪威计算中心）
Two-dimensional echocardiography (2DE) measurements of left ventricle (LV) dimensions are highly significant markers of several cardiovascular diseases. These measurements are often used in clinical care despite suffering from large variability between observers. This variability is due to the challenging nature of accurately finding the correct temporal and spatial location of measurement endpoints in ultrasound images. These images often contain fuzzy boundaries and varying reflection patterns between frames. In this work, we present a convolutional neural network (CNN) based approach to aut…
ShanghaiTech University（上海科技大学）；University of Chinese Academy of Sciences（中国科学院大学）；University of Alberta（阿尔伯塔大学）
Objective: The spinous process angle (SPA) is one of the essential parameters to denote three-dimensional (3-D) deformity of spine. We propose an automatic segmentation method based on Stacked Hourglass Network (SHN) to detect the spinous processes (SP) on ultrasound (US) spine images and to measure the SPAs of clinical scoliotic subjects. Methods: The network was trained to detect vertebral SP and laminae as five landmarks on 1200 ultrasound transverse images and validated on 100 images. All the processed transverse images with highlighted SP and laminae were reconstructed into a 3D image vol…
Dual Attention Residual U-Net for Accurate Brain Ultrasound Segmentation in IVH Detection
Chongqing Electric Power College（重庆电力学院）；Chongqing Metropolitan College of Science and Technology（重庆市级科学研究院）；Xinqiao Hospital, Army Medical University（新桥医院，军队医学大学）
