Ultrasound is a widely accessible and cost-effective medical imaging tool commonly used for prenatal evaluation of the fetal brain. However, it has limitations, particularly in the third trimester, where the complexity of the fetal brain requires high image quality for extracting quantitative data. In contrast, magnetic resonance imaging (MRI) offers superior image quality and tissue differentiation but is less available, expensive, and requires time-consuming acquisition. Thus, transforming ultrasonic images into an MRI-mimicking display may be advantageous and allow better tissue anatomy presentation. To address this goal, we have examined the use of artificial intelligence, implementing a diffusion model renowned for generating high-quality images. The proposed method, termed "Dual Diffusion Imposed Correlation" (DDIC), leverages a diffusion-based translation methodology, assuming a shared latent space between ultrasound and MRI domains. Model training was obtained utilizing the "HC18" dataset for ultrasound and the "CRL fetal brain atlas" along with the "FeTA " datasets for MRI. The generated pseudo-MRI images provide notable improvements in visual discrimination of brain tissue, especially in the lateral ventricles and the Sylvian fissure, characterized by enhanced contrast clarity. Improvement was demonstrated in Mutual information, Peak signal-to-noise ratio, Fréchet Inception Distance, and Contrast-to-noise ratio. Findings from these evaluations indicate statistically significant superior performance of the DDIC compared to other translation methodologies. In addition, a Medical Opinion Test was obtained from 5 gynecologists. The results demonstrated display improvement in 81% of the tested images. In conclusion, the presented pseudo-MRI images hold the potential for streamlining diagnosis and enhancing clinical outcomes through improved representation.
Multi-Center Study on Deep Learning-Assisted Detection and Classification of Fetal Central Nervous System Anomalies Using Ultrasound Imaging
School of Mathematics and Statistics, Xiamen University of Technology（厦门理工学院数学与统计学院）；Department of Obstetrics, Xiamen University Affiliated First Hospital（厦门大学附属第一医院妇产科）
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）
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）
SonoCLIP: Mask-Guided Region-Aware Vision-Language Pretraining for Fetal Ultrasound Analysis
School of Computer Science, Wuhan University, Wuhan, China（武汉大学计算机学院，武汉，中国）；Institute of Artificial Intelligence, Wuhan University, Wuhan, China（武汉大学人工智能研究院，武汉，中国）；Department of Ultrasound, Renmin Hospital of Wuhan University, Wuhan, China（武汉大学仁民医院超声科，武汉，中国）；National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, China（武汉大学多媒体软件国家工程研究中心，武汉，中国）；Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China（湖北省多媒体与网络通信工程重点实验室，武汉大学，武汉，中国）
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）
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）
Automatic linear measurements of the fetal brain on MRI with deep neural networks
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）
HDC: Hierarchical Distillation for Multi-level Noisy Consistency in Semi-Supervised Fetal Ultrasound Segmentation
University of Information Technology（信息科技大学）；Ho Chi Minh University of Technology（胡志明市技术大学）；University of Science（科学大学）；Vietnam National University（越南国家大学）；Boston University（波士顿大学）；Taipei Medical University（台北医学大学）；Posts and Telecommunications Institute of Technology（邮电技术学院）；New Mexico State University（新墨西哥州立大学）
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）
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）
Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slices
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）
PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images
Jinan University（暨南大学）；The University of Auckland（奥克兰大学）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；The Hong Kong University of Science and Technology（香港科技大学）；University of Girona（赫罗纳大学）；Canon Medical Systems (China) Co., LTD（佳能医疗系统（中国）有限公司）；Nankai University（南开大学）；Hunan University（湖南大学）；University College Dublin（都柏林大学学院）；Zhejiang Gongshang University（浙江工商大学）；Chongqing University of Technology（重庆理工大学）；Shenzhen University（深圳大学）；NanFang Hospital of Southern Medical University（南方医科大学南方医院）；Zhujiang Hospital of Southern Medical University（南方医科大学珠江医院）；Universitat de Barcelona（巴塞罗那大学）；Institució Catalana de Recerca i Estudis Avançats (ICREA)（加泰罗尼亚研究与高级研究机构）
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）
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）
Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries
Universitat de Barcelona（巴塞罗那大学）；HP Inc.（惠普公司）；Suez University（苏伊士大学）；Nile University（尼罗河大学）；Intixel（Intixel公司）；University of Ghana（加纳大学）；University of London（伦敦大学）；Kamuzu University of Health Sciences（卡穆祖健康科学大学）；University of M’Hamed Bougara Boumerdes（穆罕默德·布格拉·布迈德斯大学）；Algiers University（阿尔及尔大学）；Mulago National Referral and Teaching Hospital（穆拉戈国家转诊教学医院）；Makerere University College of Health Sciences（马克雷雷大学健康科学学院）
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）
Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI
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）
深度神经网络的性能通常随着训练图像的数量而增加。然而，并非所有图像都对提高性能和鲁棒性具有相同的重要性。与非病理性病例相比，在胎儿脑部MRI中，异常会加剧发育中的脑部解剖结构的变异性。少量的异常情况，通常在用于训练的临床数据集中可用，不太可能公平地代表异常发育大脑的丰富变异性。这导致通过最大化平均性能来训练的机器学习系统偏向于非病理情况。这个问题最近被称为隐藏分层。为了适合临床使用，自动分割方法需要可靠地实现高质量的分割结果，也适用于病理病例。在本文中，我们证明了最先进的深度学习管道nnU-Net很难推广到看不见的异常情况。为了缓解这个问题，我们建议训练一个深度神经网络，以最小化数据集上每体积损失分布的百分位数。我们表明，这可以通过使用分布鲁棒优化（DRO）。DRO会自动对性能较低的训练样本进行重新加权，鼓励nnU-Net在所有情况下都能更一致地执行。我们使用368例胎儿脑T2 w MRI数据集验证了我们的方法，其中包括124例开放性脊柱裂病例的MRI和51例其他严重脑发育异常病例的MRI。
胎儿肺弥散加权磁共振成像（DWI）数据的定量分析显示，在提供定量成像生物标志物，间接反映胎儿肺成熟的潜力。然而，采集期间的胎儿运动妨碍了对采集的DWI数据的定量分析，因此妨碍了可靠的临床应用。我们介绍了qDWI-morph，这是一种用于运动补偿定量DWI（qDWI）分析的无监督深度神经网络架构。我们的方法将配准子网络与定量DWI模型拟合子网络耦合。我们同时估计的qDWI参数和运动模型，通过最小化的生物物理通知损失函数集成的注册损失和模型拟合质量损失。我们证明了qDWI-morph的附加价值：1）没有运动补偿的基线qDWI分析和2）仅包含配准丢失的基线深度学习模型。通过胎儿肺DWI数据的体内qDWI分析，qDWI-形态实现了与胎龄的显著改善的相关性（R平方=0.32 vs. 0.13，0.28）。我们的qDWI-形态有可能实现DWI数据的运动补偿定量分析，并为非侵入性胎儿肺成熟度评估提供临床可行的生物标志物。我们的代码可在https://github.com/TechnionComputationalMRILab/qDWI-Morph上获取。
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）
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）
Fetal MRI by robust deep generative prior reconstruction and diffeomorphic registration: application to gestational age prediction
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）
Rapid head-pose detection for automated slice prescription of fetal-brain MRI
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）
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）
FetalCLIP: A Visual-Language Foundation Model for Fetal Ultrasound Image Analysis
Department of Computer Vision（计算机视觉系）；Mohamed bin Zayed University of Artificial Intelligence（马尔代夫比兹人工智能大学）；Department of Machine Learning（机器学习系）；Corniche Hospital, Abu Dhabi Health Services Company (SEHA)（阿布扎赫尔医院，阿布扎赫健康服务公司（SEHA））
FetalNet: Multi-task Deep Learning Framework for Fetal Ultrasound Biometric Measurements
Sano Centre for Computational Medicine（萨诺计算医学中心）；Warsaw University of Technology（华沙理工大学）；Fetai Health Ltd.（Fetai健康有限公司）；Medical University of Warsaw（华沙医科大学）；Tooploox（Tooploox公司）
AI system for fetal ultrasound in low-resource settings
Google Health（谷歌健康）；University of Zambia School of Medicine（赞比亚大学医学院）；University of North Carolina School of Medicine（北卡罗来纳大学医学院）；UNC Global Projects – Zambia, LLC（北卡罗来纳大学全球项目赞比亚有限责任公司）
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）
