A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation
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）
Jie Luo, Matt Toews, Ines Machado, Sarah Frisken, Miaomiao Zhang, Frank Preiswerk, Alireza Sedghi, Hongyi Ding, Steve Pieper, Polina Golland, Alexandra Golby, M… 展开作者
Jie Luo, Matt Toews, Ines Machado, Sarah Frisken, Miaomiao Zhang, Frank Preiswerk, Alireza Sedghi, Hongyi Ding, Steve Pieper, Polina Golland, Alexandra Golby, Masashi Sugiyama, William M. Wells
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 field from the sparse landmarks. Kernels of the GP are estimated by using variograms and a discrete grid search method. If necessary, the user can actively add new landmarks based on the image context and visualization of the uncertainty measure provided by the GP to further improve the result. We retrospectively demonstrate our registration framework as a robust and accurate brain shift compensation solution on clinical data acquired during neurosurgery.
Technische Universität München（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Although the segmentation of brain structures in ultrasound helps initialize image based registration, assist brain shift compensation, and provides interventional decision support, the task of segmenting grey and white matter in cranial ultrasound is very challenging and has not been addressed yet. We train a multi-scale fully convolutional neural network simultaneously for two classes in order to segment real clinical 3D ultrasound data. Parallel pathways working at different levels of resolution account for high frequency speckle noise and global 3D image features. To ensure reproducibility…
BrainPuzzle: Hybrid Physics and Data-Driven Reconstruction for Transcranial Ultrasound Tomography
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；University of Pittsburgh（匹兹堡大学）；Seiswave Corp（Seiswave公司）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
A benchmark for 2D foetal brain ultrasound analysis
Brain and Mind Centre, University of Sydney（悉尼大学脑与思维中心）；Faculty Of Science, Yamagata University（山形大学理学院）；Hospital Universitari de Girona Doctor Josep Trueta（赫罗纳大学医院何塞普·特鲁埃塔医生医院）
Technische Universität München（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Trinity College Dublin（都柏林圣三一学院）
Intra-operative ultrasound is an increasingly important imaging modality in neurosurgery. However, manual interaction with imaging data during the procedures, for example to select landmarks or perform segmentation, is difficult and can be time consuming. Yet, as registration to other imaging modalities is required in most cases, some annotation is necessary. We propose a segmentation method based on DeepVNet and specifically evaluate the integration of pre-training with simulated ultrasound sweeps to improve automatic segmentation and enable a fully automatic initialization of registration. I…
USFetal: Tools for Fetal Brain Ultrasound Compounding
A. I. Virtanen Institute for Molecular Sciences, Faculty of Health Sciences, University of Eastern Finland（A.I.维塔内恩分子科学研究所，健康科学学院，东芬兰大学）；Munich Center for Machine Learning (MCML) at the Technical University of Munich（慕尼黑技术大学机器学习中心（MCML））；Department of Radiology, Massachusetts General Hospital（麻省总医院放射科）；Harvard Medical School and Boston Children’s Hospital（哈佛医学院和波士顿儿童医院）
University of Oxford（牛津大学）；Leiden University Medical Center（莱顿大学医学中心）；Shanghai Jiao Tong University（上海交通大学）
Two-dimensional (2D) freehand ultrasound is the mainstay in prenatal care and fetal growth monitoring. The task of matching corresponding cross-sectional planes in the 3D anatomy for a given 2D ultrasound brain scan is essential in freehand scanning, but challenging. We propose AdLocUI, a framework that Adaptively Localizes 2D Ultrasound Images in the 3D anatomical atlas without using any external tracking sensor.. We first train a convolutional neural network with 2D slices sampled from co-aligned 3D ultrasound volumes to predict their locations in the 3D anatomical atlas. Next, we fine-tune …
Technische Universität München（慕尼黑工业大学）；Ludwig-Maximilians-University (LMU)（路德维希-马克西米利安大学（慕尼黑大学））
In this work we propose a novel approach to perform segmentation by leveraging the abstraction capabilities of convolutional neural networks (CNNs). Our method is based on Hough voting, a strategy that allows for fully automatic localisation and segmentation of the anatomies of interest. This approach does not only use the CNN classification outcomes, but it also implements voting by exploiting the features produced by the deepest portion of the network. We show that this learning-based segmentation method is robust, multi-region, flexible and can be easily adapted to different modalities. In …
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford（牛津大学工程科学系生物医学工程研究所）；Nuffield Department of Obstetrics and Gynaecology, John Radcliffe Hospital, University of Oxford（牛津大学约翰拉德克利夫医院妇产科）；Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford（牛津大学李兆基健康信息与发现中心大数据研究所）
To improve the performance of most neuroimiage analysis pipelines, brain extraction is used as a fundamental first step in the image processing. But in the case of fetal brain development, there is a need for a reliable US-specific tool. In this work we propose a fully automated 3D CNN approach to fetal brain extraction from 3D US clinical volumes with minimal preprocessing. Our method accurately and reliably extracts the brain regardless of the large data variation inherent in this imaging modality. It also performs consistently throughout a gestational age range between 14 and 31 weeks, rega…
Deep learning based Non-Rigid Volume-to-Surface Registration for Brain Shift compensation Using Point Cloud
Austrian Center for Medical Innovation and Technology（奥地利医学创新与技术中心）；Medical University of Vienna（维也纳医学大学）；University of Cambridge（剑桥大学）；Danube Private University (DPU)（多瑙私人大学（DPU））
Cross-Modal Ultrasound-MRI Learning for Fetal Brain Ventricular Volumetry and Abnormality Screening
Harvard Medical School（哈佛医学院）；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）；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）
What neurosurgeons need to see: synthetic intra-operative MRI from ultrasound for brain-shift compensation in brain tumour surgery
Department of Neurosurgery, Neurovascular Unit, Río Hortega University Hospital, Valladolid, Spain（西班牙巴利亚多利德里奥·奥尔特加大学医院神经外科神经血管科）；Specialized Group in Biomedical Imaging and Computational Analysis (GEIBAC), Instituto de Investigación Biosanitaria de Valladolid (IBioVALL), Valladolid, Spain（西班牙巴利亚多利德生物医学研究与计算分析专业组(GEIBAC)，巴利亚多利德生物健康研究所(IBioVALL)）
Concordia University（康考迪亚大学）；Université Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）；Western University（韦仕敦大学）；St. Olavs Hospital（圣奥拉夫医院）；Trondheim University Hospital（特隆赫姆大学医院）；Norwegian University of Science and Technology (NTNU)（挪威科技大学）；SINTEF Digital（SINTEF数字研究所）
Purpose: Registration and segmentation of magnetic resonance (MR) and ultrasound (US) images play an essential role in surgical planning and resection of brain tumors. However, validating these techniques is challenging due to the scarcity of publicly accessible sources with high-quality ground truth information. To this end, we propose a unique annotation dataset of tumor tissues and resection cavities from the previously published RESECT dataset (Xiao et al. 2017) to encourage a more rigorous assessments of image processing techniques. Acquisition and validation methods: The RESECT database …
We propose an unsupervised deep learning method for atlas based registration to achieve segmentation and spatial alignment of the embryonic brain in a single framework. Our approach consists of two sequential networks with a specifically designed loss function to address the challenges in 3D first trimester ultrasound. The first part learns the affine transformation and the second part learns the voxelwise nonrigid deformation between the target image and the atlas. We trained this network end-to-end and validated it against a ground truth on synthetic datasets designed to resemble the challen…
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）
New York University（纽约大学）；Tandon School of Engineering, New York University（纽约大学坦登工程学院）；Riverside Research（河滨研究院）；Skirball Institute of Biomolecular Medicine, New York University School of Medicine（纽约大学医学院斯基尔比尔分子医学研究所）
High-frequency ultrasound (HFU) is well suited for imaging embryonic mice in vivo because it is non-invasive and real-time. Manual segmentation of the brain ventricles (BVs) and whole body from 3D HFU images is time-consuming and requires specialized training. This paper presents a deep-learning-based segmentation pipeline which automates several time-consuming, repetitive tasks currently performed to study genetic mutations in developing mouse embryos. Namely, the pipeline accurately segments the BV and body regions in 3D HFU images of mouse embryos, despite significant challenges due to posi…
Whiting School of Engineering, Johns Hopkins University（约翰斯·霍普金斯大学怀廷工程学院）；Rutgers, The State University of New Jersey（罗格斯新泽西州立大学）
Automatic segmentation of anatomical landmarks from ultrasound (US) plays an important role in the management of preterm neonates with a very low birth weight due to the increased risk of developing intraventricular hemorrhage (IVH) or other complications. One major problem in developing an automatic segmentation method for this task is the limited availability of annotated data. To tackle this issue, we propose a novel image synthesis method using multi-scale self attention generator to synthesize US images from various segmentation masks. We show that our method can synthesize high-quality U…
Imperial College London（帝国理工学院）；King’s College London（伦敦国王学院）
Standard scan plane detection in fetal brain ultrasound (US) forms a crucial step in the assessment of fetal development. In clinical settings, this is done by manually manoeuvring a 2D probe to the desired scan plane. With the advent of 3D US, the entire fetal brain volume containing these standard planes can be easily acquired. However, manual standard plane identification in 3D volume is labour-intensive and requires expert knowledge of fetal anatomy. We propose a new Iterative Transformation Network (ITN) for the automatic detection of standard planes in 3D volumes. ITN uses a convolutiona…
New York University（纽约大学）；Hunter College High School（亨特学院高中）；Beihang University（北京航空航天大学）；Riverside Research（河滨研究所）；Skirball Institute of Biomolecular Medicine, New York University（纽约大学斯科博尔生物分子医学研究所）
High-frequency ultrasound (HFU) is well suited for imaging embryonic mice due to its noninvasive and real-time characteristics. However, manual segmentation of the brain ventricles (BVs) and body requires substantial time and expertise. This work proposes a novel deep learning based end-to-end auto-context refinement framework, consisting of two stages. The first stage produces a low resolution segmentation of the BV and body simultaneously. The resulting probability map for each object (BV or body) is then used to crop a region of interest (ROI) around the target object in both the original i…
MedICAL Laboratory, Polytechnique Montreal（蒙特利尔理工学院MedICAL实验室）；NED Laboratory, University of Montreal（蒙特利尔大学NED实验室）；CHU Sainte-Justine Research Center（圣尤斯汀大学医院研究中心）
Ultrasound (US) can be used to assess brain development in newborns, as MRI is challenging due to immobilization issues, and may require sedation. Dilatation of the lateral ventricles in the brain is a risk factor for poorer neurodevelopment outcomes in infants. Hence, 3D US has the ability to assess the volume of the lateral ventricles similar to clinically standard MRI, but manual segmentation is time consuming. The objective of this study is to develop an approach quantifying the ratio of lateral ventricular dilatation with respect to total brain volume using 3D US, which can assess the sev…
New York University（纽约大学）；Riverside Research（河滨研究公司）；New York University School of Medicine（纽约大学医学院）
Volumetric analysis of brain ventricle (BV) structure is a key tool in the study of central nervous system development in embryonic mice. High-frequency ultrasound (HFU) is the only non-invasive, real-time modality available for rapid volumetric imaging of embryos in utero. However, manual segmentation of the BV from HFU volumes is tedious, time-consuming, and requires specialized expertise. In this paper, we propose a novel deep learning based BV segmentation system for whole-body HFU images of mouse embryos. Our fully automated system consists of two modules: localization and segmentation. I…
University of Oxford（牛津大学）；Amazon（亚马逊）；Kings College London（伦敦国王学院）
This paper presents a novel approach to automatic fetal brain biometry motivated by needs in low- and medium- income countries. Specifically, we leverage high-end (HE) ultrasound images to build a biometry solution for low-cost (LC) point-of-care ultrasound images. We propose a novel unsupervised domain adaptation approach to train deep models to be invariant to significant image distribution shift between the image types. Our proposed method, which employs a Dual Adversarial Calibration (DAC) framework, consists of adversarial pathways which enforce model invariance to; i) adversarial perturb…
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…
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）
Fetal brain magnetic resonance imaging (MRI) offers exquisite images of the developing brain but is not suitable for second-trimester anomaly screening, for which ultrasound (US) is employed. Although expert sonographers are adept at reading US images, MR images which closely resemble anatomical images are much easier for non-experts to interpret. Thus in this paper we propose to generate MR-like images directly from clinical US images. In medical image analysis such a capability is potentially useful as well, for instance for automatic US-MRI registration and fusion. The proposed model is end…
Automatic brain tumor segmentation in 2D intra-operative ultrasound images using magnetic resonance imaging tumor annotations
Department of Health Research（健康研究系）；SINTEF Digital（SINTEF数字）；Department of Physics（物理系）；Norwegian University of Science and Technology (NTNU)（挪威科学与技术大学（NTNU））；Department of Circulation and Medical Imaging（循环与医学成像系）；St. Olavs hospital（圣奥拉夫医院）；Department of Neuromedicine and Movement Science（神经医学与运动科学系）
3D EAGAN: 3D edge-aware attention generative adversarial network for prostate segmentation in transrectal ultrasound images
Nanchang Hangkong University（南昌航空大学）；Nanjing University of Information Science and Technology（南京信息工程大学）；The First Affiliated Hospital of Nanchang University（南昌大学第一附属医院）
Measuring proximity to standard planes during fetal brain ultrasound scanning
Wellcome/EPSRC center for Interventional and Surgical Sciences (WEISS)（Wellcome/EPSRC介入与外科科学中心（WEISS））；University College London (UCL)（伦敦大学学院（UCL））；Dept of Computer Science, UCL（伦敦大学学院计算机科学系）；Dept of Obstetrics and Gynecology, University Hospital of Rennes（雷恩大学医院妇产科）；Univ Rennes, INSERM, LTSI - UMR 1099（雷恩大学、法国国家健康与医学研究院、图像与信号处理实验室 - UMR 1099）；Fetal Medicine Unit, Elizabeth Garrett Anderson Wing, UCLH NHS Foundation Trust（伦敦大学学院医院NHS基金会信托伊丽莎白·加勒特·安德森翼胎儿医学科）；Elizabeth Garrett Anderson Institute for Women’s Health, UCL（伦敦大学学院伊丽莎白·加勒特·安德森妇女健康研究所）；Dept of Obstetrics and Gynecology, UCLH（伦敦大学学院医院妇产科）
该研究提出一种半监督分割模型结合6D平面姿态回归的流水线，在NVIDIA Clara AGX边缘设备上实现实时接近度检测，可辅助胎儿脑部超声扫描的导航支持。
Shadow Reduction in Ultrasound Imaging Using Differentiable Simulation and Radiance Field Decomposition
University of Oxford（牛津大学）；University of Amsterdam（阿姆斯特丹大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔朗根-纽伦堡大学）；Imperial College London（伦敦帝国理工学院）；University of Copenhagen（哥本哈根大学）
A Systematic Benchmark of Intraoperative Ultrasound-to-MR Synthesis for Brain Tumour Surgery
Department of Neurosurgery, Neurovascular Unit Río Hortega University Hospital（里奥霍尔特ega大学医院神经外科部门，神经血管单元）；Specialized Group in Biomedical Imaging and Computational Analysis (GEIBAC)（生物医学成像与计算分析专项组（GEIBAC））；Instituto de Investigación Biosanitaria de Valladolid (IBioVALL)（瓦尔拉多利德生物医学研究 institute（IBioVALL））
Isfahan University of Technology（伊斯法罕理工大学）；University of Michigan（密歇根大学）
Reversible image watermarking guaranties restoration of both original cover and watermark logo from the watermarked image. Capacity and distortion of the image under reversible watermarking are two important parameters. In this study a reversible watermarking is investigated with focusing on increasing the embedding capacity and reducing the distortion in medical images. Integer wavelet transform is used for embedding where in each iteration, one watermark bit is embedded in one transform coefficient. We devise a novel approach that when a coefficient is modified in an iteration, the produced …
Institute of Fundamental Technological Research, Polish Academy of Sciences（波兰科学院基础技术研究所）；Medical University of Warsaw（华沙医科大学）
Convolutional neural networks (CNNs) have achieved remarkable success in medical image analysis tasks. In ultrasound (US) imaging, CNNs have been applied to object classification, image reconstruction and tissue characterization. However, CNNs can be vulnerable to adversarial attacks, even small perturbations applied to input data may significantly affect model performance and result in wrong output. In this work, we devise a novel adversarial attack, specific to ultrasound (US) imaging. US images are reconstructed based on radio-frequency signals. Since the appearance of US images depends on …
MGA-Net: A Novel Mask-Guided Attention Neural Network for Precision Neonatal Brain Imaging
Biomedical Research；Innovation Institute of C\'adiz (INiBICA) Research Unit, Puerta del Mar University, C\'adiz, Spain；Division of Neonatology, Department of Pediatrics, Puerta del Mar University Hospital, C\'adiz, Spain；Area of Pediatrics, Department of Child；Radiology, Medical School, University of C\'adiz, C\'adiz, Spain
Deep Learning Analysis of Prenatal Ultrasound for Identification of Ventriculomegaly
organization= Department of Systems；Computer Engineering, Carleton University , city= Ottawa , state= Ontario , country= Canada；organization= Department of Methodological；Implementation Research, Ottawa Hospital Research Institute , city= Ottawa , state= Ontario , country= Canada；organization= Department of Acute Care Research, Ottawa Hospital Research Institute , city= Ottawa , state= Ontario , country= Canada；organization= Children's Hospital of Eastern Ontario Research Institute , city= Ottawa , state= Ontario , country= Canada；organization= Better Outcomes Registry \& Network Ontario, Children’s Hospital of Eastern , city= Ottawa , state= Ontario , country= Canada；organization= Department of Obstetrics；Gynecology, University of Ottawa , city= Ottawa , state= Ontario , country= Canada；organization= School of Epidemiology；Public Health, University of Ottawa , city= Ottawa , state= Ontario , country= Canada；organization= Department of Obstetrics, Gynecology \& Newborn Care, The Ottawa Hospital , city= Ottawa , state= Ontario , country= Canada；Global Health Office, University of Ottawa , city= Ottawa , state= Ontario , country= Canada；organization= Department of Clinical Science；Translational Medicine, University of Ottawa , city= Ottawa , state= Ontario , country= Canada
University of Florida（佛罗里达大学）；US Anesthesia Partners（美国麻醉伙伴公司）
Automated object recognition in medical images can facilitate medical diagnosis and treatment. In this paper, we automatically segmented supraclavicular nerves in ultrasound images to assist in injecting peripheral nerve blocks. Nerve blocks are generally used for pain treatment after surgery, where ultrasound guidance is used to inject local anesthetics next to target nerves. This treatment blocks the transmission of pain signals to the brain, which can help improve the rate of recovery from surgery and significantly decrease the requirement for postoperative opioids. However, Ultrasound Guid…
Fetal brain magnetic resonance imaging (MRI) offers exquisite images of the developing brain but is not suitable for anomaly screening. For this ultrasound (US) is employed. While expert sonographers are adept at reading US images, MR images are much easier for non-experts to interpret. Hence in this paper we seek to produce images with MRI-like appearance directly from clinical US images. Our own clinical motivation is to seek a way to communicate US findings to patients or clinical professionals unfamiliar with US, but in medical image analysis such a capability is potentially useful, for in…
Fusing Radiomic Features with Deep Representations for Gestational Age Estimation in Fetal Ultrasound Images
Research Ireland Centre for Research Training in Machine Learning（爱尔兰研究培训机器学习研究中心）；School of Medicine, University College Dublin（都柏林大学医学院）；School of Veterinary Medicine, University College Dublin（都柏林大学兽医学院）；School of Computer Science, University College Dublin（都柏林大学计算机科学学院）
From Pre- to Intra-operative MRI: Predicting Brain Shift in Temporal Lobe Resection for Epilepsy Surgery
Surgical and Interventional Engineering（外科与介入工程）；King’s College London（伦敦国王学院）；National Hospital for Neurology and Neurosurgery（神经外科医院）；NHS（国家健康服务）；Neurosurgery, King’s College Hospital（神经外科，伦敦国王学院医院）；Fondazione IRCCS Ca’ Granda（IRCCS Ca’ Granda基金会）；Ospedale Maggiore di Milano（米兰大医院）
Effective, robust, and automatic tools for brain tumor segmentation are needed for the extraction of information useful in treatment planning from magnetic resonance (MR) images. Context-aware artificial intelligence is an emerging concept for the development of deep learning applications for computer-aided medical image analysis. In this work, it is investigated whether the addition of contextual information from the brain anatomy in the form of white matter, gray matter, and cerebrospinal fluid masks and probability maps improves U-Net-based brain tumor segmentation. The BraTS2020 dataset wa…
