Fetal MRI is heavily constrained by unpredictable and substantial fetal motion that causes image artifacts and limits the set of viable diagnostic image contrasts. Current mitigation of motion artifacts is predominantly performed by fast, single-shot MRI and retrospective motion correction. Estimation of fetal pose in real time during MRI stands to benefit prospective methods to detect and mitigate fetal motion artifacts where inferred fetal motion is combined with online slice prescription with low-latency decision making. Current developments of deep reinforcement learning (DRL), offer a novel approach for fetal landmarks detection. In this task 15 agents are deployed to detect 15 landmarks simultaneously by DRL. The optimization is challenging, and here we propose an improved DRL that incorporates priors on physical structure of the fetal body. First, we use graph communication layers to improve the communication among agents based on a graph where each node represents a fetal-body landmark. Further, additional reward based on the distance between agents and physical structures such as the fetal limbs is used to fully exploit physical structure. Evaluation of this method on a repository of 3-mm resolution in vivo data demonstrates a mean accuracy of landmark estimation within 10 mm of ground truth as 87.3%, and a mean error of 6.9 mm. The proposed DRL for fetal pose landmark search demonstrates a potential clinical utility for online detection of fetal motion that guides real-time mitigation of motion artifacts as well as health diagnosis during MRI of the pregnant mother.
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…
MIT（麻省理工学院）；Tsinghua University（清华大学）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）
The performance and diagnostic utility of magnetic resonance imaging (MRI) in pregnancy is fundamentally constrained by fetal motion. Motion of the fetus, which is unpredictable and rapid on the scale of conventional imaging times, limits the set of viable acquisition techniques to single-shot imaging with severe compromises in signal-to-noise ratio and diagnostic contrast, and frequently results in unacceptable image quality. Surprisingly little is known about the characteristics of fetal motion during MRI and here we propose and demonstrate methods that exploit a growing repository of MRI ob…
Northeastern University（东北大学）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）
Fetal brain segmentation is an important first step for slice-level motion correction and slice-to-volume reconstruction in fetal MRI. Fast and accurate segmentation of the fetal brain on fetal MRI is required to achieve real-time fetal head pose estimation and motion tracking for slice re-acquisition and steering. To address this critical unmet need, in this work we analyzed the speed-accuracy performance of a variety of deep neural network models, and devised a symbolically small convolutional neural network that combines spatial details at high resolution with context features extracted at …
Enhancing Generalized Fetal Brain MRI Segmentation using A Cascade Network with Depth-wise Separable Convolution and Attention Mechanism
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）；MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence（教育部计算神经科学与类脑智能重点实验室）；MOE Frontiers Center for Brain Science（教育部脑科学前沿中心）
The Hebrew University of Jerusalem（耶路撒冷希伯来大学）；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）；Tel Aviv University（特拉维夫大学）；Tel Aviv Sourasky Medical Center（特拉维夫苏拉斯基医疗中心）；University of Ottawa（渥太华大学）；Children's Hospital of Eastern Ontario（东安大略儿童医院）；Shaare Zedek Medical Center（沙雷泽德克医疗中心）
Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and long-term risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are Cerebral Biparietal Diameter (CBD), Bone Biparietal Diameter (BBD), and Trans-Cerebellum Diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error. The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements…
Imperial College London（伦敦帝国学院）；King’s College London（伦敦国王学院）；FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；Technical University of Munich（慕尼黑工业大学）
Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development. Accurate segmentation of the different brain tissues is a vital step in several brain analysis tasks, such as cortical surface reconstruction and tissue thickness measurements. Fetal MRI scans, however, are prone to motion artifacts that can affect the correctness of both manual and automatic segmentation techniques. In this paper, we propose a novel network structure that can simultaneously generate conditional atlases and predict brain tissue segmentation, called CAS-Net. The condition…
DCRA-Net: Attention-Enabled Reconstruction Model for Dynamic Fetal Cardiac MRI
King’s College London（伦敦国王学院）；Evelina London Children’s Hospital（伊芙琳娜伦敦儿童医院）；Imperial College London（帝国理工学院）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学右岸医院）
The Hebrew University of Jerusalem（耶路撒冷希伯来大学）；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）；Tel Aviv University（特拉维夫大学）
Volumetric measurements of fetal structures in MRI are time consuming and error prone and therefore require automatic segmentation. Placenta segmentation and accurate fetal brain segmentation for gyrification assessment are particularly challenging because of the placenta fuzzy boundaries and the fetal brain cortex complex foldings. In this paper, we study the use of the Contour Dice loss for both problems and compare it to other boundary losses and to the combined Dice and Cross-Entropy loss. The loss is computed efficiently for each slice via erosion, dilation and XOR operators. We describe …
University of Electronic Science and Technology of China（电子科技大学）；King’s College London（伦敦国王学院）；University Hospitals KU Leuven（鲁汶大学附属医院）；University College London（伦敦大学学院）；SenseTime Research（商汤科技研究院）
Segmentation of the fetal brain from stacks of motion-corrupted fetal MRI slices is important for motion correction and high-resolution volume reconstruction. Although Convolutional Neural Networks (CNNs) have been widely used for automatic segmentation of the fetal brain, their results may still benefit from interactive refinement for challenging slices. To improve the efficiency of interactive refinement process, we propose an Uncertainty-Guided Interactive Refinement (UGIR) framework. We first propose a grouped convolution-based CNN to obtain multiple automatic segmentation predictions with…
Improving cross-domain brain tissue segmentation in fetal MRI with synthetic data
Lausanne University Hospital（洛桑大学医院）；University of Lausanne (UNIL)（洛桑大学）；CIBM Center for Biomedical Imaging（CIBM生物医学成像中心）；King’s College London（伦敦国王学院）；FAU Erlangen-Nuremberg（埃尔朗根-纽伦堡大学）
University Medical Center Utrecht（乌得勒支大学医学中心）；Wilhelmina Children’s Hospital（威廉明娜儿童医院）；Utrecht University（乌得勒支大学）
MR images of fetuses allow clinicians to detect brain abnormalities in an early stage of development. The cornerstone of volumetric and morphologic analysis in fetal MRI is segmentation of the fetal brain into different tissue classes. Manual segmentation is cumbersome and time consuming, hence automatic segmentation could substantially simplify the procedure. However, automatic brain tissue segmentation in these scans is challenging owing to artifacts including intensity inhomogeneity, caused in particular by spontaneous fetal movements during the scan. Unlike methods that estimate the bias f…
The Challenge of Fetal Cardiac MRI Reconstruction Using Deep Learning
King’s College London（伦敦国王学院）；Technical University of Munich（慕尼黑工业大学）；Imperial College London（帝国理工学院）；Guy’s & St. Thomas’ NHS Foundation Trust（盖伊和圣托马斯国民保健服务基金会信托）；Evelina London Children’s Hospital（伊夫琳娜伦敦儿童医院）
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…
Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and cortical folding. Manual segmentation of the cortical plate, or manual refinement of automatic segmentations is tedious and time-consuming. Automatic segmentation of the cortical plate, on the other hand, is challenged by the relatively low resolution of the reconstructed fetal brain MRI scans compared to the thin structure of the cortical plate, partial voluming, and the wide range of variations in the morphology of the cortical plate as the brain matures during gestation. To reduce the burde…
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Tencent AI Lab（腾讯人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）
Recently, deep neural networks have greatly advanced undersampled Magnetic Resonance Image (MRI) reconstruction, wherein most studies follow the one-anatomy-one-network fashion, i.e., each expert network is trained and evaluated for a specific anatomy. Apart from inefficiency in training multiple independent models, such convention ignores the shared de-aliasing knowledge across various anatomies which can benefit each other. To explore the shared knowledge, one naive way is to combine all the data from various anatomies to train an all-round network. Unfortunately, despite the existence of th…
Equivariant symmetry-aware head pose estimation for fetal MRI
MIT Computer Science and Artificial Intelligence Laboratory（麻省理工学院计算机科学与人工智能实验室）；Boston Children's Hospital and Harvard Medical School（波士顿儿童医院和哈佛医学院）；Inria, Université Côte d'Azur（法国国家科学研究中心（Inria）、蔚蓝海岸大学）
Aligning Fetal Anatomy with Kinematic Tree Log-Euclidean PolyRigid Transforms
Computer Science and Artificial Intelligence Lab, MIT, Cambridge, USA（麻省理工学院计算机科学与人工智能实验室）；California Institute of Technology, Pasadena, USA（加州理工学院）；Boston Children’s Hospital and Harvard Medical School, Boston, USA（哈佛医学院波士顿儿童医院）；The Hebrew University of Jerusalem, Jerusalem, Israel（耶路撒冷希伯来大学）
Quantitative analysis of fetal lung Diffusion-Weighted MRI (DWI) data shows potential in providing quantitative imaging biomarkers that indirectly reflect fetal lung maturation. However, fetal motion during the acquisition hampered quantitative analysis of the acquired DWI data and, consequently, reliable clinical utilization. We introduce qDWI-morph, an unsupervised deep-neural-network architecture for motion compensated quantitative DWI (qDWI) analysis. Our approach couples a registration sub-network with a quantitative DWI model fitting sub-network. We simultaneously estimate the qDWI param…
Evaluating Synthetic Data Generation for Domain Generalization in Fetal Brain MRI Segmentation
Department of Radiology, Lausanne University Hospital and University of Lausanne (UNIL)（拉沃斯大学医院放射科和洛桑大学（UNIL））；CIBM Center for Biomedical Imaging（生物医学成像中心）；Institute for Information Processing, Leibniz University Hannover（汉诺威莱比锡大学信息处理研究所）；Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London（伦敦国王学院生物医学工程系）；Department of Biomedical Imaging and Image-Guided Therapy, Division of Neuroradiology and Musculoskeletal Radiology, Medical University of Vienna（维也纳医学大学生物医学成像与影像引导治疗系）；Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab (CIR), Medical University of Vienna（维也纳医学大学生物医学成像与影像引导治疗系，计算成像研究实验室（CIR））；Christian Doppler Laboratory for Mathematical Modelling and Simulation of Next-Generation Medical Ultrasound Devices, Medical University of Vienna（维也纳医学大学下一代医学超声设备数学建模与仿真克里斯蒂安多普勒实验室）；Comprehensive Center for Artificial Intelligence in Medicine, Medical University of Vienna（维也纳医学大学人工智能在医学中的综合中心）；Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image–guided Therapy, Medical University of Vienna（维也纳医学大学生物医学成像与影像引导治疗系，神经放射学和骨科放射学系）
AtlasSeg: Atlas Prior Guided Dual-U-Net for Cortical Segmentation in Fetal Brain MRI
College of Biomedical Engineering & Instrument Science, Zhejiang University（浙江大学生物医学工程与仪器科学学院）；Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences（中国医学科学院北京医院国家老年医学中心老年医学研究所）；Shandong Provincial Hospital Affiliated to Shandong First Medical University（山东第一医科大学附属山东省立医院）；State Key Lab of CAD&CG, Zhejiang University（浙江大学CAD&CG国家重点实验室）
University Hospitals Leuven（鲁汶大学医院）；University Hospitals Tanta（坦塔大学医院）；University College London（伦敦大学学院）
Deep neural networks have increased the accuracy of automatic segmentation, however, their accuracy depends on the availability of a large number of fully segmented images. Methods to train deep neural networks using images for which some, but not all, regions of interest are segmented are necessary to make better use of partially annotated datasets. In this paper, we propose the first axiomatic definition of label-set loss functions that are the loss functions that can handle partially segmented images. We prove that there is one and only one method to convert a classical loss function for fu…
Towards contrast- and pathology-agnostic clinical fetal brain MRI segmentation using SynthSeg
University Children’s Hospital Zurich（苏黎世大学儿童医院）；ETH Zurich（苏黎世联邦理工学院）；University of Zurich（苏黎世大学）；Medical University of Vienna（维也纳医科大学）；CIBM Center for Biomedical Imaging（CIBM生物医学成像中心）；University of Lausanne（洛桑大学）；Lausanne University Hospital（洛桑大学医院）；King's College London（伦敦国王学院）；University Hospital Erlangen（埃尔朗根大学医院）；Spanish National Research Council (CSIC)（西班牙国家研究委员会）；Universitat Politècnica de València（瓦伦西亚理工大学）
Universidad Politécnica de Madrid（马德里理工大学）；Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)（生物医学研究网络中心（生物工程、生物材料与纳米医学））；King’s College London（伦敦国王学院）
Magnetic resonance imaging of whole fetal body and placenta is limited by different sources of motion affecting the womb. Usual scanning techniques employ single-shot multi-slice sequences where anatomical information in different slices may be subject to different deformations, contrast variations or artifacts. Volumetric reconstruction formulations have been proposed to correct for these factors, but they must accommodate a non-homogeneous and non-isotropic sampling, so regularization becomes necessary. Thus, in this paper we propose a deep generative prior for robust volumetric reconstructi…
The Hebrew University of Jerusalem（耶路撒冷希伯来大学）；Sagol Brain Institute, Tel Aviv Medical Center（特拉维夫医疗中心萨戈尔脑研究所）；Tel Aviv Medical Center（特拉维夫医疗中心）；Tel Aviv University（特拉维夫大学）；Tel Aviv Sourasky Medical Center（特拉维夫苏拉斯基医疗中心）
Normal fetal adipose tissue (AT) development is essential for perinatal well-being. AT, or simply fat, stores energy in the form of lipids. Malnourishment may result in excessive or depleted adiposity. Although previous studies showed a correlation between the amount of AT and perinatal outcome, prenatal assessment of AT is limited by lacking quantitative methods. Using magnetic resonance imaging (MRI), 3D fat- and water-only images of the entire fetus can be obtained from two point Dixon images to enable AT lipid quantification. This paper is the first to present a methodology for developing …
In this paper we present a novel method for the correction of motion artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of the whole uterus. Contrary to current slice-to-volume registration (SVR) methods, requiring an inflexible anatomical enclosure of a single investigated organ, the proposed patch-to-volume reconstruction (PVR) approach is able to reconstruct a large field of view of non-rigidly deforming structures. It relaxes rigid motion assumptions by introducing a specific amount of redundant information that is exploited with parallelized patch-wise optimization…
Deep Learning-Based Fetal Lung Segmentation from Diffusion-weighted MRI Images and Lung Maturity Evaluation for Fetal Growth Restriction
School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK（生物医学工程与成像科学学院，伦敦国王学院）；Elizabeth Garrett Anderson Institute for Women’s Health, University College, London, UK（埃利扎贝塔·加德纳妇女健康研究所，大学学院，伦敦）；University College London Hospital NHS Foundation Trust, London, UK（伦敦大学学院医院国家健康服务信托基金）；Department of Medical Physics and Biomedical Engineering, University College London Hospitals, London, UK（医学物理与生物医学工程系，伦敦大学学院医院）；Department of Radiology, University College London Hospitals NHS Foundation Trust, London, United Kingdom（放射科，伦敦大学学院医院国家健康服务信托基金）
Detailed delineation of the fetal brain in diffusion MRI via multi-task learning
Harvard Medical School（哈佛医学院）；Elmhurst Hospital Center（埃尔姆赫斯特医院中心）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Massachusetts General Hospital（麻省总医院）；University of California Irvine（加州大学欧文分校）
Department of Radiology, Massachusetts General Hospital（麻省总医院放射科）；Radiology, Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院放射科）
In fetal-brain MRI, head-pose changes between prescription and acquisition present a challenge to obtaining the standard sagittal, coronal and axial views essential to clinical assessment. As motion limits acquisitions to thick slices that preclude retrospective resampling, technologists repeat ~55-second stack-of-slices scans (HASTE) with incrementally reoriented field of view numerous times, deducing the head pose from previous stacks. To address this inefficient workflow, we propose a robust head-pose detection algorithm using full-uterus scout scans (EPI) which take ~5 seconds to acquire. …
A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation
University Hospitals Leuven（鲁汶大学医院）；Technical University Munich（慕尼黑工业大学）；University Hospital Zurich（苏黎世大学医院）；University of Zurich（苏黎世大学）；University College London（伦敦大学学院）；University Hospitals Tanta（坦塔大学医院）；Medical University of Vienna（维也纳医科大学）
Annotation-free deep learning for detection and segmentation of fetal germinal matrix-intraventricular hemorrhage in brain MRI
Department of Radiology, West China Second University Hospital, Sichuan University（四川大学华西第二医院放射科）；School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University（清华大学医学院生物医学工程系）；Department of Radiology, Sichuan Provincial Woman’s and Children’s Hospital, The Affiliated Women’s and Children’s Hospital of Chengdu Medical College（四川省妇幼保健院放射科，成都医学院附属妇幼医院）；Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China（成都妇女儿童中央医院，电子科技大学医学院）；Department of Radiology, The Third Affiliated Hospital of Zhengzhou University（郑州大学第三附属医院放射科）；Qujing Maternal and Child Health Hospital, Qujing, China（曲靖 maternal and child health hospital, Qujing, China）
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）；Department of Radiology, University Hospitals Leuven, Belgium（鲁汶大学医院放射科）；Institute for Women’s Health, University College London, UK（伦敦大学学院妇女健康研究所）；Department of Obstetrics and Gynaecology, University Hospitals Leuven, Belgium（鲁汶大学医院妇产科）；Department of Gynecology and Obstetrics, University Hospitals Tanta, Egypt（坦塔大学医院妇产科）；Department of Biomedical Imaging and Image-guided Therapy Medical University of Vienna, Austria（维也纳医科大学生物医学成像与图像引导治疗系）
The performance of deep neural networks typically increases with the number of training images. However, not all images have the same importance towards improved performance and robustness. In fetal brain MRI, abnormalities exacerbate the variability of the developing brain anatomy compared to non-pathological cases. A small number of abnormal cases, as is typically available in clinical datasets used for training, are unlikely to fairly represent the rich variability of abnormal developing brains. This leads machine learning systems trained by maximizing the average performance to be biased t…
ResNet-34 with Lightweight Decoder for Accurate and Efficient Segmentation of Fetal Brain MRI
Department of Biomedical Physics and Technology, University of Dhaka（达卡大学生物医学物理与技术系）；Department of Electrical Engineering, College of Engineering, Qatar University（卡塔尔大学工程学院电气工程系）；Department of Biomedical Engineering, Jashore University of Science and Technology（贾沙尔大学科学与技术学院生物医学工程系）
MRAnnotator: multi-Anatomy and many-Sequence MRI segmentation of 44 structures
BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院生物医学工程与影像研究所）；Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院诊断、分子与介入放射学系）；Rotterdam School of Management, Erasmus University Rotterdam（伊拉斯姆斯大学鹿特丹管理学院）；East River Medical Imaging（东河医学影像中心）；Department of Radiology, Cornell Medicine（康奈尔医学院放射学系）；Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院温赖克人工智能与人类健康系）
BrainNormalizer: Anatomy-Informed Pseudo-Healthy Brain Reconstruction from Tumor MRI via Edge-Guided ControlNet
University of Pittsburgh（匹兹堡大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Cedars-Sinai Medical Center（西德萨斯医疗中心）；Mayo Clinic Arizona（梅奥诊所亚利桑那分部）
Self-Supervised Slice-to-Volume Reconstruction with Gaussian Representations for Fetal MRI
Department of Electrical and Electronic Engineering and I-X, Imperial College London（伦敦帝国理工学院电气与电子工程系及I-X）；Trinity College Institute of Neuroscience, Trinity College Dublin（都柏林圣三一学院神经科学研究所）
Automated Fetal Brain MRI Biometry in Healthy and Pathological Cases
Faculty of Medicine, University of Ljubljana（卢布尔雅那大学医学院）；University Medical Centre Ljubljana（卢布尔雅那大学医学中心）；Faculty of Electrical Engineering, University of Ljubljana（卢布尔雅那大学电气工程学院）
Unsupervised Segmentation of Fetal Brain MRI using Deep Learning Cascaded Registration
Universitat Pompeu Fabra（庞培法布拉大学）；BCNatal；Institución Catalana de Investigación y Estudios Avanzados (ICREA)（加泰罗尼亚高级研究与发展研究院）；Centre for Biomedical Research on Rare Diseases (CIBERER)（罕见疾病生物医学研究中心）；European Commission, Joint Research Centre (JRC)（欧盟委员会联合研究中心）
McGill University（麦吉尔大学）；MILA (Québec Artificial Intelligence Institute)（MILA（魁北克人工智能研究所））；University of Edinburgh（爱丁堡大学）；The Alan Turing Institute（艾伦·图灵研究所）
We propose a hierarchically structured variational inference model for accurately disentangling observable evidence of disease (e.g. brain lesions or atrophy) from subject-specific anatomy in brain MRIs. With flexible, partially autoregressive priors, our model (1) addresses the subtle and fine-grained dependencies that typically exist between anatomical and pathological generating factors of an MRI to ensure the clinical validity of generated samples; (2) preserves and disentangles finer pathological details pertaining to a patient's disease state. Additionally, we experiment with an alternat…
Contrastive Anatomy-Contrast Disentanglement: A Domain-General MRI Harmonization Method
Chair for AI for Image-Guided Diagnosis and Therapy, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany（人工智能辅助影像引导诊断与治疗研究所，慕尼黑技术大学（TUM）及慕尼黑技术大学医院，慕尼黑，德国）；Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心，慕尼黑，德国）；Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany（人工智能在医疗与健康领域研究所，慕尼黑技术大学（TUM）及慕尼黑技术大学医院，慕尼黑，德国）；Department of Radiation Oncology, TUM University Hospital, Munich, Germany（放射肿瘤科，慕尼黑技术大学医院，慕尼黑，德国）；Chair for Computer Vision and Artificial Intelligence, Technical University of Munich (TUM), Munich, Germany（计算机视觉与人工智能研究所，慕尼黑技术大学（TUM），慕尼黑，德国）；Stanford Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Stanford, CA, USA（斯坦福大学人工智能在医学与影像中心，斯坦福大学，斯坦福，CA，美国）；Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany（德国转化癌症研究联盟（DKTK），慕尼黑合作伙伴站点，慕尼黑，德国）；Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany（放射医学研究所（IRM），辐射科学部门（DRS），海德堡慕尼黑研究中心，慕尼黑，德国）
Pose-GuideNet: Automatic Scanning Guidance for Fetal Head Ultrasound from Pose Estimation
Institute of Biomedical Engineering, University of Oxford（牛津大学生物医学工程研究所）；Nuffield Department of Women’s & Reproductive Health, University of Oxford（牛津大学纳菲尔德妇女与生殖健康系）
The University of Queensland（昆士兰大学）；CSIRO（联邦科学与工业研究组织）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
Deep neural networks have been extensively studied for undersampled MRI reconstruction. While achieving state-of-the-art performance, they are trained and deployed specifically for one anatomy with limited generalization ability to another anatomy. Rather than building multiple models, a universal model that reconstructs images across different anatomies is highly desirable for efficient deployment and better generalization. Simply mixing images from multiple anatomies for training a single network does not lead to an ideal universal model due to the statistical shift among datasets of various…
