Ultrasound diagnosis is routinely used in obstetrics and gynecology for fetal biometry, and owing to its time-consuming process, there has been a great demand for automatic estimation. However, the automated analysis of ultrasound images is complicated because they are patient-specific, operator-dependent, and machine-specific. Among various types of fetal biometry, the accurate estimation of abdominal circumference (AC) is especially difficult to perform automatically because the abdomen has low contrast against surroundings, non-uniform contrast, and irregular shape compared to other parameters.We propose a method for the automatic estimation of the fetal AC from 2D ultrasound data through a specially designed convolutional neural network (CNN), which takes account of doctors' decision process, anatomical structure, and the characteristics of the ultrasound image. The proposed method uses CNN to classify ultrasound images (stomach bubble, amniotic fluid, and umbilical vein) and Hough transformation for measuring AC. We test the proposed method using clinical ultrasound data acquired from 56 pregnant women. Experimental results show that, with relatively small training samples, the proposed CNN provides sufficient classification results for AC estimation through the Hough transformation. The proposed method automatically estimates AC from ultrasound images. The method is quantitatively evaluated, and shows stable performance in most cases and even for ultrasound images deteriorated by shadowing artifacts. As a result of experiments for our acceptance check, the accuracies are 0.809 and 0.771 with the expert 1 and expert 2, respectively, while the accuracy between the two experts is 0.905. However, for cases of oversized fetus, when the amniotic fluid is not observed or the abdominal area is distorted, it could not correctly estimate AC.
MS-UMamba: An Improved Vision Mamba Unet for Fetal Abdominal Medical Image Segmentation
Guangxi Key Laboratory of Machine Vision and Intelligent Control（广西机器视觉与智能控制重点实验室）；Wuzhou University（梧州大学）；College of Electronical and Information Engineering（电子信息工程学院）；Wuzhou Medical College（梧州医学院）；School of Microelectronics（微电子学院）；Southern Medical University（南方医科大学）
Fetal Head and Abdomen Measurement Using Convolutional Neural Network, Hough Transform, and Difference of Gaussian Revolved along Elliptical Path (Dogell) Algorithm
Faculty of Computer Science Universitas Indonesia（印度尼西亚大学计算机科学学院）
Automatic quality assessment for 2D fetal sonographic standard plane based on multi-task learning
Sichuan University West China Second Hospital（四川大学华西第二医院）；Glasgow College, University of Electronic Science and Technology of China（电子科技大学格拉斯哥学院）；Wangwang Technology Company（旺旺科技公司）
Medical-Knowledge Driven Multiple Instance Learning for Classifying Severe Abdominal Anomalies on Prenatal Ultrasound
Medical Ultrasound Image Computing (MUSIC) Lab（医学超声图像计算（MUSIC）实验室）；School of Biomedical Engineering（生物医学工程学院）；Medical School（医学院）；Shenzhen University（深圳大学）；Shenzhen RayShape Medical Technology Co., Ltd（深圳RayShape医疗科技有限公司）；Center for Medical Ultrasound（医学超声中心）；The Affiliated Suzhou Hospital of Nanjing Medical University（南京医科大学附属苏州医院）；Suzhou Municipal Hospital（苏州 municipal 医院）；Gusu School（古苏学校）；Nanjing Medical University（南京医科大学）；The First Hospital of China Medical University（中国医科大学第一医院）；Qilu Hospital of Shandong University（山东大学齐鲁医院）；Nanjing Women and Children's Healthcare Hospital（南京妇女儿童保健医院）；Women's Hospital of Naniing Medical University（南京医科大学妇女医院）；Maternal and Child Health Hospital of Hubei Province（湖北省妇幼保健医院）；Northwest Women’s and Children’s Hospital（西北妇女儿童医院）；School of Artificial Intelligence（人工智能学院）；National Engineering Laboratory for Big Data System Computing Technology（大数据系统计算技术国家工程实验室）；School of Biomedical Engineering and Informatics（生物医学工程与信息学院）
Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks
Imperial College London（帝国理工学院）；ETH Zurich（苏黎世联邦理工学院）；Guy’s and St Thomas’ NHS Foundation（盖伊和圣托马斯国民保健服务基金会）
从胎儿超声图像中测量头部生物特征对于监测胎儿的健康发育具有重要意义。然而，相关解剖结构的准确测量在临床中受到较大的观察者间差异的影响。为了解决这个问题，提出了一种利用全卷积网络（FCN）来确定胎儿头围（HC）和双顶径（BPD）测量值的自动化方法。在常规筛查检查期间，在头部的大约2000个2D超声图像上训练FCN，所述图像具有由45个不同的超声医师提供的注释，以执行头部的语义分割。椭圆被拟合到所得到的分割轮廓，以模仿通常由声谱仪操作员产生的注释。该模型的性能与观察者间的变异性进行了比较，其中两位专家手动注释了100张测试图像。HC的平均绝对模型专家误差略优于观察者间误差（1.99 mm vs 2.16 mm），BPD（0.61 mm vs 0.59 mm）以及Dice系数（0.980 vs 0.980）相当。我们的研究结果表明，该模型的性能与人类专家相似，并学会从由许多超声医师注释的大型数据集中产生准确的预测。此外，在GPU上以15 fps的速度近实时生成测量结果，这可以加快熟练和实习超声医师的临床工作流程。
Segmenting Fetal Head with Efficient Fine-tuning Strategies in Low-resource Settings: an empirical study with U-Net
Science Foundation Ireland Centre for Research Training in Machine Learning（爱尔兰科学基金会机器学习研究培训中心）；School of Medicine, University College Dublin（都柏林大学医学院）；School of Computer Science, University College Dublin（都柏林大学计算机科学学院）
A multi-centre, multi-device benchmark dataset for landmark-based comprehensive fetal biometry
Department of Computer Science and UCL Hawkes Institute, University College London, London WC1E 6BT, UK（计算机科学系和UCL Hawkes研究所，伦敦大学学院，伦敦WC1E 6BT，英国）；Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel（萨戈尔脑研究所，特拉维夫 Sourasky 医疗中心，以色列特拉维夫）；School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel（计算机科学与工程学院，耶路撒冷希伯来大学，耶路撒冷，以色列）；UCLH NHS Foundation Trust and the Elizabeth Garrett Anderson Institute for Women’s Health, UCL, London, UK（UCLH NHS基金会信托和Elizabeth Garrett Anderson妇女健康研究所，UCL，伦敦，英国）；Sagol School of Neuroscience and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel（萨戈尔神经科学学院和Sackler医学院，特拉维夫大学，以色列特拉维夫）
Improving Automatic Fetal Biometry Measurement with Swoosh Activation Function
University of Sydney（悉尼大学）；James Cook University（詹姆斯库克大学）；Central Queensland University（中央昆士兰大学）；Liverpool Hospital（利物浦医院）；Sydney Medical School Nepean（悉尼医学学院尼皮恩分校）
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）
