The automatic analysis of ultrasound sequences can substantially improve the efficiency of clinical diagnosis. In this work we present our attempt to automate the challenging task of measuring the vascular diameter of the fetal abdominal aorta from ultrasound images. We propose a neural network architecture consisting of three blocks: a convolutional layer for the extraction of imaging features, a Convolution Gated Recurrent Unit (C-GRU) for enforcing the temporal coherence across video frames and exploiting the temporal redundancy of a signal, and a regularized loss function, called \textit{CyclicLoss}, to impose our prior knowledge about the periodicity of the observed signal. We present experimental evidence suggesting that the proposed architecture can reach an accuracy substantially superior to previously proposed methods, providing an average reduction of the mean squared error from $0.31 mm^2$ (state-of-art) to $0.09 mm^2$, and a relative error reduction from $8.1\%$ to $5.3\%$. The mean execution speed of the proposed approach of 289 frames per second makes it suitable for real time clinical use.
Deep Supervision by Gaussian Pseudo-label-based Morphological Attention for Abdominal Aorta Segmentation in Non-Contrast CTs
Univ Rennes（雷恩大学）；CHU Rennes（雷恩大学医院）；Inserm（法国国家健康与医学研究院）；Centre de Recherche en Information Biomédicale Sino-français (CRIBs)（中法生物医学信息研究中心）；Southeast University（东南大学）
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）
Contrast-agent-induced deterministic component of CT-density in the abdominal aorta during routine angiography: proof of concept study
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow（莫斯科卫生部诊断与远程医疗技术研究与实践临床中心）；Bauman Moscow State Technical University（鲍曼莫斯科国立技术大学）；Federal Research Center "Computer Science and Control" of Russian Academy of Sciences（俄罗斯科学院计算机科学与控制联邦研究中心）；I.M. Sechenov First Moscow State Medical University (Sechenov University)（谢切诺夫第一莫斯科国立医科大学（谢切诺夫大学））
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）
CACTUSS: Common Anatomical CT-US Space for US examinations
Technical University of Munich（慕尼黑工业大学）；Sharif University of Technology（谢里夫理工大学）；Klinikum rechts der Isar（慕尼黑 Rechts der Isar 医院）；John Hopkins University（约翰·霍普金斯大学）
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）
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）
3D convolutional neural network for abdominal aortic aneurysm segmentation
Vicomtech Foundation（维克姆泰克基金会）；Biodonostia Health Research Institute（比奥多诺斯蒂亚健康研究所）；Universitat Pompeu Fabra（庞培法布拉大学）；Donostia University Hospital（多诺斯蒂亚大学医院）；ICREA（加泰罗尼亚高等研究院）
