Ultrasound Image Segmentation

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5 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Latest papers 44

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Compute-Optimal Network Design for Echocardiography Myocardial Segmentation and Perfusion Quantification using Neural Scaling Laws
Jun 4, 2026Clara Rodrigo González, Matthieu Toulemonde, Lasha Gvinianidze +5Ultrasound Image Segmentation Medical Image Analysis
Department of Bioengineering Imperial College London London, UK · National Heart and Lung Institute Imperial College London Guy’s and St. Thomas’ NHS Foundation Trust
Implicit Fuzzification via Bounded Noise Injection for Robust Medical Image Segmentation
Jun 3, 2026Bisheng Tang, Zhangfeng Ma, Chuchu Zhai +4Ultrasound Image Segmentation Medical Image Segmentation
School of Information Science and Engineering, Provincial Key Laboratory of Informational Service for Rural Area of Southwestern Hunan, Shaoyang University, Shaoyang 422000, China · Xinshao County People’s Hospital, Shaoyang 422900, China
BBR-Net: Boundary-Balanced Replay for Continual Medical Image Segmentation
Jun 2, 2026Zahid Ullah, Sieun Choi, Jihie KimInteractive Medical Image Segmentation Ultrasound Image Segmentation
Department of Computer Science and Artificial Intelligence, Dongguk University, Seoul, 04620, Republic of Korea
Robust Cross-Domain Generalization Using Unlabeled Target Data with Source-Domain Supervision
May 27, 2026Yuyue Zhou, Shrimanti Ghosh, Michael +7Ultrasound Image Segmentation Unsupervised Domain Adaptation
Kai Yue · Department of Radiology and Diagnostic Imaging, University of Alberta, 8303 112 St NW, Edmonton, T6G 2T4, AB, Canada
Cesarean Scar Defect Segmentation in Transvaginal Ultrasound Images: a Dataset and Benchmark
May 26, 2026Yuan Tian, Yue Li, Wei Xia +10Ultrasound Image Segmentation Ultrasound Imaging
Department of Obstetrics and Gynecology, International Peace Maternity and Child Health Hospital affiliated to Shanghai Jiao Tong University School of Medicine, 910 Hengshan Road, Xuhui District, 200030, Shanghai, China. · School of Computer Science, University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo, 315100, Zhejiang, China. · School of Computer Science, University of Nottingham, University Park, Nottingham, NG7 2RD, UK. +4
R2AoP: Reliable and Robust Angle of Progression Estimation from Intrapartum Ultrasound
May 20, 2026Yuanhan Wang, Yifei Chen, Beining Wu +7Ultrasound Image Segmentation Ultrasound Imaging
Tsinghua University, Beijing, China · Hangzhou Dianzi University, Hangzhou, China · Shenzhen Research Institute of Big Data, Shenzhen, China
Synergistic Foundation Models for Semi-Supervised Fetal Cardiac Ultrasound Analysis: SAM-Med2D Boundary Refinement and DINOv3 Semantic Enhancement
May 19, 2026Tonghao Zhuang, Shanglong Hu, Yongsheng Luo +2Ultrasound Image Segmentation Medical Imaging Foundation Models
Zhuhai College of Science and Technology, Zhuhai, China
Rad-VLSM: A Cross-Modal Framework with Semantics-Assisted Prompting for Medical Segmentation and Diagnosis
May 18, 2026Fengyi Zhang, Xujie Zeng, Mohan Liu +2Ultrasound Image Segmentation Cross-Modality Medical Image Segmentation
EAGT: Echocardiography Augmentation for Generalisability and Transferability
May 14, 2026Soroush Elyasi, Sara Adibzadeh, Nasim Dadashi Serej +1Ultrasound Image Segmentation Data Augmentation
THRIVE Centre, University of West London, London, United Kingdom
A CUBS-Compatible Ultrasound Morphology and Uncertainty-Aware Baseline for Carotid Intima-Media Segmentation and Preliminary Risk Prediction
May 14, 2026Aueaphum AueawatthanaphisutUltrasound Image Segmentation Clinical Risk Prediction
School of Information, Computer, and Communication Technology Sirindhorn International Institute of Technology, Thammasat University Pathum Thani, Thailand
Learning to Segment using Summary Statistics and Weak Supervision
May 4, 2026Omkar Kulkarni, Edward Raff, Tim OatesUltrasound Image Segmentation Image Segmentation
University of Maryland, Baltimore County, Baltimore MD, USA · Crowdstrike, Austin TX, USA
Protect the Brain When Treating the Heart: A Convolutional Neural Network for Detecting Emboli
Apr 24, 2026Andrea Angino, Ken Trotti, Diego Ulisse Pizzagalli +3Ultrasound Image Segmentation Image Segmentation
Universit`a della Svizzera Italiana (USI), Center for Computational Medicine in Cardiology (CCMC) · Unidistance Suisse · King Abdullah University of Science and Technology (KAUST), AMCS/CEMSE Division +2
Attention-ResUNet for Automated Fetal Head Segmentation
Apr 20, 2026Ammar Bhilwarawala, Mainak BandyopadhyayUltrasound Image Segmentation Medical Image Segmentation
School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India
Adaptive Bidirectional Task Interaction for Joint Segmentation and Classification of Breast Ultrasound
Mar 1, 2026Abdullah Al Shafi, Md Kawsar Mahmud Khan Zunayed, Safin Ahmmed +2Ultrasound Image Segmentation Image Segmentation
Khulna University of Engineering and Technology, Bangladesh · University Clermont Auvergne, France

Jun 4, 2026, eess.IVJ/K move · Enter open · S save

Compute-Optimal Network Design for Echocardiography Myocardial Segmentation and Perfusion Quantification using Neural Scaling Laws

Clara Rodrigo González, Matthieu Toulemonde, Lasha Gvinianidze, Cameron A. B. Smith+4

Department of Bioengineering Imperial College London London, UK · National Heart and Lung Institute Imperial College London Guy’s and St. Thomas’ NHS Foundation Trust

Ultrasound Image Segmentation Medical Image Analysis Echocardiography Ultrasound Imaging Neural Scaling Laws

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Myocardial perfusion quantification using contrast-enhanced ultrasound offers a bedside non-ionizing alternative to nuclear imaging modalities. However, its clinical adoption is hindered by time-consuming manual labelling. Automated segmentation has proved challenging due to a paucity of in-domain training data. Adapting strategies currently used to optimise large language models for large datasets, we apply neural scaling laws to predict network performance for myocardial segmentation. We extrapolate performance on subsets of the data to determine optimal network size on the CAMUS echocardiography dataset and a 25-patient contrast-enhanced ultrasound (CEUS) dataset. Finally, we validate the clinical utility of our models by comparing the final myocardial perfusion parameters with those obtained by a senior cardiologist. Extrapolation based on the scaling law is predictive of test loss at the full dataset size, allowing us to select two networks that obtained state-of-the-art performance on CAMUS with a 240-fold reduction in parameter count. We observe the gradient of the scaling law transfers from CAMUS to the CEUS dataset with a bias in the predicted losses. The automatically segmented masks perform equivalently to a senior cardiologist in myocardial perfusion quantification. These results establish neural scaling laws as a practical tool for data-driven compute-optimal model design for small imaging datasets.

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