Fetal ultrasound standard plane classification is essential for reliable prenatal diagnosis but faces inherent challenges, including low tissue contrast, boundary ambiguity, and operator-dependent image quality variations. To overcome these limitations, we propose a plug-and-play adaptive contrast adjustment module (ACAM), whose core design is inspired by the clinical practice of doctors adjusting image contrast to obtain clearer and more discriminative structural information. The module employs a shallow texture-sensitive network to predict clinically plausible contrast parameters, transforms input images into multiple contrast-enhanced views through differentiable mapping, and fuses them within downstream classifiers. Validated on a multi-center dataset of 12,400 images across six anatomical categories, the module consistently improves performance across diverse models, with accuracy of lightweight models increasing by 2.02 percent, accuracy of traditional models increasing by 1.29 percent, and accuracy of state-of-the-art models increasing by 1.15 percent. The innovation of the module lies in its content-aware adaptation capability, replacing random preprocessing with physics-informed transformations that align with sonographer workflows while improving robustness to imaging heterogeneity through multi-view fusion. This approach effectively bridges low-level image features with high-level semantics, establishing a new paradigm for medical image analysis under real-world image quality variations.
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）
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…
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）
Enhancing Corpus Callosum Segmentation in Fetal MRI via Pathology-Informed Domain Randomization
Univeristat Politècnica de Catalunya, Barcelona, Spain（巴塞罗那理工大学）；Department of Radiology, Lausanne University Hospital and University of Lausanne (UNIL), Lausanne, Switzerland（拉索恩大学医院放射科）；University of Lausanne (UNIL), Lausanne, Switzerland（洛桑大学）；CIBM Center for Biomedical Imaging, Lausanne, Switzerland（瑞士拉索恩生物医学成像中心）；BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), Universitat de Barcelona, Barcelona, Spain（巴塞罗那大学BCNatal胎儿医学研究中心）；Department Woman-Mother-Child, CHUV, Lausanne, Switzerland（瑞士拉索恩大学医院妇女-母亲-儿童部门）
Origin Health India（印度Origin Health公司）；Bangalore Fetal Medicine Center（班加罗尔胎儿医学中心）；Prime Imaging and Prenatal Diagnostics（Prime影像与产前诊断中心）；Kasturba Medical College（卡斯图尔巴医学院）；Manipal Academy of Higher Education（马尼帕尔高等教育学院）；Origin Health（Origin Health公司）
Multiple studies have demonstrated that obtaining standardized fetal brain biometry from mid-trimester ultrasonography (USG) examination is key for the reliable assessment of fetal neurodevelopment and the screening of central nervous system (CNS) anomalies. Obtaining these measurements is highly subjective, expertise-driven, and requires years of training experience, limiting quality prenatal care for all pregnant mothers. In this study, we propose a deep learning (DL) approach to compute 3 key fetal brain biometry from the 2D USG images of the transcerebellar plane (TC) through the accurate …
Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes
ShanghaiTech University（上海科技大学）；Fudan University（复旦大学）；Obstetrics and Gynecology Hospital of Fudan University（复旦大学附属妇产科医院）；Northwestern Polytechnical University（西北工业大学）；Shanghai United Imaging Intelligence company Ltd.（上海联影智能科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究中心）
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）
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…
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…
Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge
organization= Department of Radiology, Lausanne University Hospital；University of Lausanne , city= Lausanne , country= Switzerland；organization= CIBM Center for Biomedical Imaging , city= Lausanne , country= Switzerland；organization= Department of Early Life Imaging, School of Biomedical Engineering \& Imaging Sciences, King’s College London , city= London , country= UK；organization= Smart Imaging Lab, University Hospital Erlangen , city= Erlangen , country= Germany；organization= Center for MR-Research, University Children’s Hospital Zurich, University of Zurich , city= Zurich , country= Switzerland；organization= Neuroscience Center Zurich, University of Zurich , city= Zurich , country= Switzerland；organization= National Heart \& Lung Institute, Imperial College London , city= London , country= UK；organization= University of California, San Francisco；UCSF Benioff Children’s Hospital , city= San Francisco , state= California , country= USA；organization= Department of Quantitative Biomedicine, University of Zurich , city= Zurich , country= Switzerland；organization= Department of Informatics, Technical University of Munich , city= Munich , country= Germany；organization= Boston Children’s Hospital, Harvard Medical School , city= Boston , state= Massachusetts , country= USA；organization= Neuroimaging Unit, Scientific Institute IRCCS E. Medea , city= Bosisio Parini , country= Italy；organization= Department of Informatics, Systems；Communication, University of Milano Bicocca , city= Milan , country= Italy；organization= Research Institute of Computer Vision；organization= BCN MedTech, Department of Engineering, Universitat Pompeu Fabra , city= Barcelona , country= Spain；organization= Department of Information Engineering, University of Padova , city= Padova , country= Italy；organization= Institut Pasteur, Université Paris Cité, CNRS UMR 3571, Decision；organization= Inria, HeKA, PariSantéCampus , city= Paris , country= France；organization= L. D. College of Engineering , city= Gujarat , country= India；organization= Medical Faculty Heidelberg, Heidelberg University , addressline= Pattern Analysis；Learning Group, Department of Radiation Oncology, Heidelberg University Hospital , city= Heidelberg , country= Germany；organization= Canon Medical Systems (China) Co., Ltd , city= , country= China；organization= Faculty of Electrical Engineering, University of Ljubljana , city= Ljubljana , country= Slovenia；organization= Department of Radiology, Seoul National University Hospital , city= Seoul , country= South Korea；organization= School of Mechanical；Electrical Engineering, University of Electronic Science；organization= School of Computer Science, Wuhan University , city= Wuhan , country= China；Developmental Science Center, Boston Children’s Hospital, Harvard Medical School , city= Boston , state= Massachusetts , country= USA；organization= Hawkes Institute, Department of Computer Science, University College London , city= London , country= UK；organization= Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School , city= Charlestown , state= Massachusetts , country= USA；organization= Department of Biomedical Imaging；Image-guided Therapy, Computational Imaging Research Lab (CIR), Early Life Image Analysis Group, Medical University of Vienna , city= Vienna , country= Austria；organization= University Research Priority Project Adaptive Brain Circuits in Development；Learning (AdaBD), University of Zurich , city= Zurich , country= Switzerland；organization= Sagol Brain Institute, Tel Aviv Sourasky Medical Center；School of EE, Tel-Aviv University , city= Tel-Aviv , country= Israel；organization= Department of Medical Imaging Sciences, The Faculty of Social Welfare；Health Sciences, University of Haifa , city= Haifa , country= Israel；Faculty of Medicine；Sagol School of Neuroscience, Tel-Aviv University , city= Tel-Aviv , country= Israel；organization= Department Woman-Mother-Child, CHUV , city= Lausanne , country= Switzerland；organization= BCNatal Fetal Medicine Research Center (Hospital Clínic；Hospital Sant Joan de Déu), Universitat de Barcelona , city= Barcelona , country= Spain；organization= German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing , city= Heidelberg , country= Germany；organization= Helmholtz Imaging, German Cancer Research Center (DKFZ) , city= Heidelberg , country= Germany；organization= Faculty of Mathematics；Computer Science, Heidelberg University , city= Heidelberg , country= Germany；organization= University of Zurich , city= Zurich , country= Switzerland；organization= Croatian Institute for Brain Research, School of Medicine, University of Zagreb , city= Zagreb , country= Croatia；organization= Department of Biomedical Engineering, School of Biomedical Engineering \& Imaging Sciences, King’s College , city= London , country= United Kingdom；Musculoskeletal Radiology, Medical University of Vienna , city= Vienna , country= Austria；organization= Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School , city= Boston , state= Massachusetts , country= USA；organization= Department of Radiology, Boston Children’s Hospital, Harvard Medical School , city= Boston , state= Massachusetts , country= USA
