Cardiac motion estimation is critical to the assessment of cardiac function. Myocardium feature tracking (FT) can directly estimate cardiac motion from cine MRI, which requires no special scanning procedure. However, current deep learning-based FT methods may result in unrealistic myocardium shapes since the learning is solely guided by image intensities without considering anatomy. On the other hand, motion estimation through learning is challenging because ground-truth motion fields are almost impossible to obtain. In this study, we propose a novel Anatomy-Aware Tracker (AATracker) for cardiac motion estimation that preserves anatomy by weak supervision. A convolutional variational autoencoder (VAE) is trained to encapsulate realistic myocardium shapes. A baseline dense motion tracker is trained to approximate the motion fields and then refined to estimate anatomy-aware motion fields under the weak supervision from the VAE. We evaluate the proposed method on long-axis cardiac cine MRI, which has more complex myocardium appearances and motions than short-axis. Compared with other methods, AATracker significantly improves the tracking performance and provides visually more realistic tracking results, demonstrating the effectiveness of the proposed weakly-supervision scheme in cardiac motion estimation.
United Imaging Intelligence（联影智能）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Oregon（俄勒冈大学）
Motion estimation of cardiac MRI videos is crucial for the evaluation of human heart anatomy and function. Recent researches show promising results with deep learning-based methods. In clinical deployment, however, they suffer dramatic performance drops due to mismatched distributions between training and testing datasets, commonly encountered in the clinical environment. On the other hand, it is arguably impossible to collect all representative datasets and to train a universal tracker before deployment. In this context, we proposed a novel fast online adaptive learning (FOAL) framework: an o…
University of Edinburgh（爱丁堡大学）；Canon Medical Research Europe（佳能医疗欧洲研究中心）；The Alan Turing Institute（艾伦·图灵研究所）
Acquiring annotated data at scale with rare diseases or conditions remains a challenge. It would be extremely useful to have a method that controllably synthesizes images that can correct such underrepresentation. Assuming a proper latent representation, the idea of a "latent vector arithmetic" could offer the means of achieving such synthesis. A proper representation must encode the fidelity of the input data, preserve invariance and equivariance, and permit arithmetic operations. Motivated by the ability to disentangle images into spatial anatomy (tensor) factors and accompanying imaging (ve…
3D Cardiac Anatomy Generation Using Mesh Latent Diffusion Models
Department of Computer Science, University of Oxford（计算机科学系，牛津大学）；Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford（生物医学工程研究所，工程科学系，牛津大学）；Visual Geometry Group, Department of Engineering Science, University of Oxford（视觉几何组，工程科学系，牛津大学）；Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford（心血管医学部，拉德克利夫医学系，牛津大学）
King’s College London（伦敦国王学院）；Ultromics Ltd.（Ultromics有限公司）
Accurate geometric quantification of the human heart is a key step in the diagnosis of numerous cardiac diseases, and in the management of cardiac patients. Ultrasound imaging is the primary modality for cardiac imaging, however acquisition requires high operator skill, and its interpretation and analysis is difficult due to artifacts. Reconstructing cardiac anatomy in 3D can enable discovery of new biomarkers and make imaging less dependent on operator expertise, however most ultrasound systems only have 2D imaging capabilities. We propose both a simple alteration to the Pix2Vox++ networks fo…
CineMyoPS: Segmenting Myocardial Pathologies from Cine Cardiac MR
School of Medical Imaging, Fujian Medical University（福建医科大学医学影像学院）；College of Physics and Information Engineering, Fuzhou University（福州大学物理与信息工程学院）；Department of Biomedical Engineering, National University of Singapore（新加坡国立大学生物医学工程系）；School of Data Science, Fudan University（复旦大学数据科学学院）；Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院仁济医院放射科）
University of Oxford（牛津大学）；Tel-Aviv University（特拉维夫大学）
Self-supervised contrastive representation learning offers the advantage of learning meaningful visual representations from unlabeled medical datasets for transfer learning. However, applying current contrastive learning approaches to medical data without considering its domain-specific anatomical characteristics may lead to visual representations that are inconsistent in appearance and semantics. In this paper, we propose to improve visual representations of medical images via anatomy-aware contrastive learning (AWCL), which incorporates anatomy information to augment the positive/negative pa…
Fudan University（复旦大学）；Imperial College London（帝国理工学院）；University of Edinburgh（爱丁堡大学）；MRC London Institute of Medical Sciences（英国医学研究委员会伦敦医学科学研究所）；National Heart Research Institute, Singapore（新加坡国家心脏研究所）
In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures. However, due to the limit of acquisition duration and respiratory/cardiac motion, stacks of multi-slice 2D images are acquired in clinical routine. The segmentation of these images provides a low-resolution representation of cardiac anatomy, which may contain artefacts caused by motion. Here we propose a novel latent optimisation framework that jointly performs motion correction and super resolution for cardiac image segmentations. Give…
Intensity-based 3D motion correction for cardiac MR images
Munich Center for Machine Learning, Technical University Munich（慕尼黑工业大学慕尼黑机器学习中心）；School of Computation, Information and Technology, Technical University Munich（慕尼黑工业大学计算、信息与技术学院）；Klinikum Rechts der Isar, Technical University Munich（慕尼黑工业大学右岸医院）；Department of Computing, Imperial College London（伦敦帝国理工学院计算系）
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Georgia Institute of Technology（佐治亚理工学院）
Thoracic disease detection from chest radiographs using deep learning methods has been an active area of research in the last decade. Most previous methods attempt to focus on the diseased organs of the image by identifying spatial regions responsible for significant contributions to the model's prediction. In contrast, expert radiologists first locate the prominent anatomical structures before determining if those regions are anomalous. Therefore, integrating anatomical knowledge within deep learning models could bring substantial improvement in automatic disease classification. Motivated by …
Cardiac Magnetic Resonance Imaging (CMR) is widely used since it can illustrate the structure and function of heart in a non-invasive and painless way. However, it is time-consuming and high-cost to acquire the high-quality scans due to the hardware limitation. To this end, we propose a novel end-to-end trainable network to solve CMR video super-resolution problem without the hardware upgrade and the scanning protocol modifications. We incorporate the cardiac knowledge into our model to assist in utilizing the temporal information. Specifically, we formulate the cardiac knowledge as the period…
View planning for the acquisition of cardiac magnetic resonance imaging (CMR) requires acquaintance with the cardiac anatomy and remains a challenging task in clinical practice. Existing approaches to its automation relied either on an additional volumetric image not typically acquired in clinic routine, or on laborious manual annotations of cardiac structural landmarks. This work presents a clinic-compatible and annotation-free system for automatic CMR view planning. The system mines the spatial relationship -- more specifically, locates and exploits the intersecting lines -- between the sour…
University of Notre Dame（圣母大学）；Guangdong Cardiovascular Institute（广东省心血管病研究所）；Guangdong Provincial People’s Hospital（广东省人民医院）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；University of Pittsburgh（匹兹堡大学）
Real-time cardiac magnetic resonance imaging (MRI) plays an increasingly important role in guiding various cardiac interventions. In order to provide better visual assistance, the cine MRI frames need to be segmented on-the-fly to avoid noticeable visual lag. In addition, considering reliability and patient data privacy, the computation is preferably done on local hardware. State-of-the-art MRI segmentation methods mostly focus on accuracy only, and can hardly be adopted for real-time application or on local hardware. In this work, we present the first hardware-aware multi-scale neural archite…
Rutgers University（罗格斯大学）；SenseBrain Research（SenseBrain研究院）；University of Arkansas for Medical Sciences（阿肯色大学医学院）；Lund University（隆德大学）；New York University（纽约大学）
Joint 2D cardiac segmentation and 3D volume reconstruction are fundamental to building statistical cardiac anatomy models and understanding functional mechanisms from motion patterns. However, due to the low through-plane resolution of cine MR and high inter-subject variance, accurately segmenting cardiac images and reconstructing the 3D volume are challenging. In this study, we propose an end-to-end latent-space-based framework, DeepRecon, that generates multiple clinically essential outcomes, including accurate image segmentation, synthetic high-resolution 3D image, and 3D reconstructed volu…
Multi-Disease-Aware Training Strategy for Cardiac MR Image Segmentation
College of Computer Science, Sichuan Normal University（四川师范大学计算机科学学院）；School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算与人工智能学院）；West China Hospital, Sichuan University（四川大学华西医院）
Focus, Align, and Diffuse: Time-Series-Aware Keyframe Diffusion for Cardiac Dynamic Synthesis from Sparse Observations
School of Engineering, University of Birmingham（伯明翰大学工程学院）；William Harvey Research Institute, Queen Mary University London（伦敦大学玛丽女王学院威廉·哈维研究所）；Division of Cardiology, Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院心脏病学系）
RSFR: A Coarse-to-Fine Reconstruction Framework for Diffusion Tensor Cardiac MRI with Semantic-Aware Refinement
Imperial College London（帝国理工学院伦敦分校）；National Heart and Lung Institute（国家心肺研究所）；Cardiovascular Research Centre（心血管研究中心）；Royal Brompton Hospital（皇家布里蒙特医院）；School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区生物医学工程学院）；Robotics and Autonomous Systems Thrust & Data Science and Analytics Thrust, HKUST (GZ)（香港科技大学（广州）机器人与自主系统 thrust 及数据科学与分析 thrust）；Yau Mathematical Sciences Centre, Tsinghua University（清华大学姚数学科学中心）；Department of Applied Mathematics and Theoretical Physics, University of Cambridge（剑桥大学应用数学与理论物理系）；School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）
Sano Centre for Computational Medicine（萨诺计算医学中心）；Informatics Institute, University of Amsterdam（阿姆斯特丹大学信息学研究所）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Cardiac Magnetic Resonance Imaging is commonly used for the assessment of the cardiac anatomy and function. The delineations of left and right ventricle blood pools and left ventricular myocardium are important for the diagnosis of cardiac diseases. Unfortunately, the movement of a patient during the CMR acquisition procedure may result in motion artifacts appearing in the final image. Such artifacts decrease the diagnostic quality of CMR images and force redoing of the procedure. In this paper, we present a Multi-task Swin UNEt TRansformer network for simultaneous solving of two tasks in the …
ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学类脑智能科学与技术研究院）；School of Cyber Science and Engineering, Sichuan University（四川大学网络空间安全学院）
University of Maryland（马里兰大学）；PAII Inc.（PAII公司）；Alibaba Group DAMO Academy（阿里巴巴集团达摩院）；Xpeng Motors（小鹏汽车）；Ping An Technology（平安科技）；Chang Gung Memorial Hospital（长庚纪念医院）
Osteoporosis is a common chronic metabolic bone disease often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e.g. via Dual-energy X-ray Absorptiometry (DXA). This paper proposes a method to predict BMD from Chest X-ray (CXR), one of the most commonly accessible and low-cost medical imaging examinations. Our method first automatically detects Regions of Interest (ROIs) of local CXR bone structures. Then a multi-ROI deep model with transformer encoder is developed to exploit both local and global information in the chest X-ray image for ac…
School of Biomedical Engineering & Imaging Sciences, King’s College London, UK（伦敦国王学院生物医学工程与成像科学学院）；Technical University of Munich, Germany（慕尼黑工业大学）；Biomedical Image Analysis Group, Imperial College London, UK（帝国理工学院生物医学图像分析组）；Department of Adult and Paediatric Cardiology, Guy’s and St Thomas’ NHS Foundation Trust, London, UK（盖伊和圣托马斯国民健康服务信托基金会成人及儿科心脏病科）；Coimbra Institute for Biomedical Imaging and Translational Research, University of Coimbra, Portugal（科英布拉大学科英布拉生物医学成像与转化研究学院）；Helmholtz Center Munich, Germany（慕尼黑亥姆霍兹中心）
Cine cardiac MRI is routinely acquired for the assessment of cardiac health, but the imaging process is slow and typically requires several breath-holds to acquire sufficient k-space profiles to ensure good image quality. Several undersampling-based reconstruction techniques have been proposed during the last decades to speed up cine cardiac MRI acquisition. However, the undersampling factor is commonly fixed to conservative values before acquisition to ensure diagnostic image quality, potentially leading to unnecessarily long scan times. In this paper, we propose an end-to-end quality-aware c…
Magnetic Resonance Imaging (MRI) plays an important role in diagnosing the parotid tumor, where accurate segmentation of tumors is highly desired for determining appropriate treatment plans and avoiding unnecessary surgery. However, the task remains nontrivial and challenging due to ambiguous boundaries and various sizes of the tumor, as well as the presence of a large number of anatomical structures around the parotid gland that are similar to the tumor. To overcome these problems, we propose a novel anatomy-aware framework for automatic segmentation of parotid tumors from multimodal MRI. Fir…
BioMedIA Group, Department of Computing, Imperial College London, UK（伦敦帝国理工学院计算系BioMedIA小组）；School of Biomedical Engineering & Imaging Sciences, King’s College London, UK（伦敦国王学院生物医学工程与成像科学学院）；Guys and St Thomas’ Hospital, London, UK（伦敦盖伊和圣托马斯医院）
Evaluation of predictive deep learning (DL) models beyond conventional performance metrics has become increasingly important for applications in sensitive environments like healthcare. Such models might have the capability to encode and analyse large sets of data but they often lack comprehensive interpretability methods, preventing clinical trust in predictive outcomes. Quantifying uncertainty of a prediction is one way to provide such interpretability and promote trust. However, relatively little attention has been paid to how to include such requirements into the training of the model. In t…
Z 2 Sky Technologies Inc.（Z 2 天工科技公司）；University of Rochester（罗切斯特大学）；Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS（中国科学院计算技术研究所 中国科学院智能信息处理重点实验室）；University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advance Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）
Spinal surgery planning necessitates automatic segmentation of vertebrae in cone-beam computed tomography (CBCT), an intraoperative imaging modality that is widely used in intervention. However, CBCT images are of low-quality and artifact-laden due to noise, poor tissue contrast, and the presence of metallic objects, causing vertebra segmentation, even manually, a demanding task. In contrast, there exists a wealth of artifact-free, high quality CT images with vertebra annotations. This motivates us to build a CBCT vertebra segmentation model using unpaired CT images with annotations. To overco…
Imperial College London（帝国理工学院）；Technical University Munich（慕尼黑工业大学）；University of Edinburgh（爱丁堡大学）；MRC London Institute of Medical Sciences（MRC伦敦医学科学研究所）
Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR) imaging enables the assessment of regional myocardial function and is important for understanding and analyzing cardiovascular disease. However, 3D cardiac motion estimation is challenging because the acquired cine CMR images are usually 2D slices which limit the accurate estimation of through-plane motion. To address this problem, we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion fi…
Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation. However, cine CMR acquisition is inherently slow and in recent decades considerable effort has been put into accelerating scan times without compromising image quality or the accuracy of derived results. In this paper, we present a fully-automated, quality-controlled integrated framework for reconstruction, segmentation and downstream analysis of undersampled cine CMR data. The framework enables active acquisition of radial k-space data, in which acquisition can be stopped as soon as …
Positron Emission Tomography (PET) is an important tool for studying Alzheimer's disease (AD). PET scans can be used as diagnostics tools, and to provide molecular characterization of patients with cognitive disorders. However, multiple tracers are needed to measure glucose metabolism (18F-FDG), synaptic vesicle protein (11C-UCB-J), and $β$-amyloid (11C-PiB). Administering multiple tracers to patient will lead to high radiation dose and cost. In addition, access to PET scans using new or less-available tracers with sophisticated production methods and short half-life isotopes may be very limit…
Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images
School of Biomedical Engineering & Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）；Guy's and St Thomas' Hospital（盖伊和圣托马斯医院）；BioMedIA Group, Department of Computing, Imperial College London（帝国理工学院计算系生物医学图像分析组）
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…
Fairness-Aware Data Augmentation for Cardiac MRI using Text-Conditioned Diffusion Models
Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM)（巴塞罗那人工智能在医学实验室（BCN-AIM））；Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain（数学与计算机科学系，巴塞罗那大学，西班牙）；Helmholtz Center Munich（海德堡研究中心慕尼黑）；Technical University of Munich（慕尼黑技术大学）；William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London, UK（威廉·哈维研究所，英国国家健康研究院巴特研究所，伦敦女王玛丽大学，查尔斯广场，伦敦，英国）；Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London, UK（巴特心脏中心，圣巴塞洛缪医院，巴特健康国家卫生信托，西史密斯菲尔德，伦敦，英国）；Institució Catalana de Recerca i Estudis Avançats (ICREA)（加泰罗尼亚高级研究与教育研究所（ICREA））
Anatomy-aware and acquisition-agnostic joint registration with SynthMorph
Athinoula A. Martinos Center for Biomedical Imaging（阿西努拉·A·马蒂诺斯生物医学成像中心）；Department of Radiology, Massachusetts General Hospital（麻省总医院放射科）；Department of Radiology, Harvard Medical School（哈佛医学院放射科）；Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）
