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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Jun 4, 2026eess.IV

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

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.

Clara Rodrigo González, Matthieu Toulemonde, Lasha Gvinianidze +5

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 SegmentationMedical Image AnalysisEchocardiographyUltrasound ImagingNeural Scaling Laws
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Jun 3, 2026cs.CV

Implicit Fuzzification via Bounded Noise Injection for Robust Medical Image Segmentation

Image segmentation remains fundamentally limited by boundary ambiguity arising from sampling-induced information loss and inherent uncertainty in pixel-wise labeling. Although encoder-decoder architectures such as U-Net achieve strong performance, they often produce overconfident predictions that fail to capture transition-region ambiguity. To address this issue, we propose \textbf{NoiseUNet}, a simple yet effective framework that injects bounded perturbations into skip connections to regularize cross-scale feature fusion. This mechanism enforces robustness to local feature variations and promotes boundary-aware representations. Theoretically, the perturbation induces an implicit fuzzification effect, yielding soft, data-driven memberships without requiring explicit fuzzy modeling. We further introduce \textbf{ThyR}, a real-world thyroid ultrasound dataset with inherently ambiguous boundaries. Experiments demonstrate that NoiseUNet consistently improves both segmentation accuracy and boundary fidelity.

Bisheng Tang, Zhangfeng Ma, Chuchu Zhai +4

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

Ultrasound Image SegmentationMedical Image SegmentationBoundary-Aware SegmentationSegmentation Uncertainty EstimationSemantic SegmentationImplicit Regularization
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Jun 2, 2026cs.CV

BBR-Net: Boundary-Balanced Replay for Continual Medical Image Segmentation

Continual learning for medical image segmentation remains challenging under domain shift because replay-based methods often preserve appearance information without explicitly modeling anatomical structure. This study investigates whether structural consistency governs knowledge retention in continual cardiac ultrasound segmentation. We propose the Boundary-Balanced Replay Network (BBR-Net), which selects replay samples using boundary-aware priority and class balance to preserve anatomically informative regions. The method is evaluated on CAMUS and CardiacNet under forward (CAMUS to CardiacNet) and reverse (CardiacNet to CAMUS) task orders. In the forward setting, BBR-Net retains source-task performance close to an offline joint-training reference, while markedly reducing catastrophic forgetting and preserving competitive target-task adaptation. Ablation results show that boundary-aware prioritization contributes to retention and improves the balance between source-task preservation and target-task adaptation when combined with class-aware sampling. In contrast, the reverse setting reveals that structure-aware replay fails when initial representations are learned from noisy and structurally inconsistent data. To isolate this effect, we conduct a controlled structural perturbation analysis by progressively corrupting source-task boundaries while keeping the dataset, architecture, and training protocol fixed. Forgetting increases consistently as structural reliability decreases, suggesting that replay effectiveness is strongly influenced by the quality of stored structural information, rather than by memory capacity alone. These findings indicate that preserving anatomical structure under domain shift is a central factor in continual medical image segmentation, and that replay mechanisms should account for structural reliability to support robust knowledge retention.

Zahid Ullah, Sieun Choi, Jihie Kim

Department of Computer Science and Artificial Intelligence, Dongguk University, Seoul, 04620, Republic of Korea

Interactive Medical Image SegmentationUltrasound Image SegmentationReplay-Based Continual LearningMedical Image SegmentationCatastrophic Forgetting
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May 27, 2026cs.CV

Robust Cross-Domain Generalization Using Unlabeled Target Data with Source-Domain Supervision

It is often desirable to generalize medical imaging AI models trained with dense annotations to data acquired from different ultrasound scanners or clinical sites; however, retraining these models with new annotations is often difficult and costly. We examine this challenge in pediatric wrist fracture assessment using point-of-care ultrasound (POCUS), where fractures are common and can be effectively triaged via ultrasound. AI has shown radiologist-level performance for fracture detection, often aided by high-quality bony structure segmentation. However, due to significant domain shifts, models perform poorly on data from other centers or probes, and obtaining segmentation labels across devices is impractical due to manual annotation effort and data privacy concerns. To address this, we propose a target-informed self-supervised pretraining and model-ensemble strategy. Specifically, our approach combines masked image modeling (MIM) and contrastive learning to learn target-domain structural representations without labels, and introduces a confidence-aware infusion head to adaptively integrate predictions. The source dataset, collected with a Philips Lumify probe, contained dense labels, while the target dataset, acquired with a TeleMED portable probe, was unlabeled. The datasets were kept strictly separate throughout the entire process. Our method used labeled source data for supervised training and leveraged target-domain pretraining to improve generalization. On 318 images from 62 pediatric POCUS videos, this approach significantly improved cross-device performance, achieving over 6% Dice improvement on the target domain versus the baseline. These results demonstrate a label-efficient and privacy-preserving approach for cross-device-robust ultrasound AI, offering a framework that can be extended to multi-center studies or federated learning setups.

Yuyue Zhou, Shrimanti Ghosh, Michael +7

Kai Yue · Department of Radiology and Diagnostic Imaging, University of Alberta, 8303 112 St NW, Edmonton, T6G 2T4, AB, Canada

Ultrasound Image SegmentationUnsupervised Domain AdaptationCross-Dataset Generalization
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May 26, 2026cs.CV

Cesarean Scar Defect Segmentation in Transvaginal Ultrasound Images: a Dataset and Benchmark

Cesarean Scar Defect (CSD) is one of the most prevalent complications following cesarean delivery. Transvaginal ultrasonography is widely used for primary CSD screening. Accurate determination of CSD outline and dimensions is crucial for treatment. However, CSDs are frequently overlooked by sonographers due to small size and irregular morphology, suboptimal image quality, and limited clinical awareness in resource-constrained settings. Despite artificial intelligence advances in medical imaging, no public dataset exists for transvaginal ultrasound CSD segmentation. To address this gap, we present a comprehensive CSD dataset comprising 1,111 images and 16 videos, yielding 501 positive samples with confirmed CSD and precise pixel-level manual annotations. Annotations are performed following standardized clinical guidelines through collaboration between experienced sonographers and trained PhD students. This work provides high-quality benchmark resources for advancing medical image segmentation algorithms and promoting clinical innovation. Ultimately, improved CSD diagnosis and subsequent treatment strategies can enhance the quality of life in women of reproductive age, representing significant value for both medical research and clinical practice.

Yuan Tian, Yue Li, Wei Xia +10

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

Ultrasound Image SegmentationUltrasound ImagingMedical Image SegmentationMedical Image Benchmarks
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May 20, 2026cs.CV

R2AoP: Reliable and Robust Angle of Progression Estimation from Intrapartum Ultrasound

Accurate estimation of the Angle of Progression (AoP) from intrapartum transperineal ultrasound is critical for objective assessment of labor progression, yet remains highly sensitive to imaging noise, boundary ambiguities, and the geometric amplification of local segmentation errors. We propose R2AoP, a reliable and robust AoP estimation framework that integrates structurally informed segmentation and confidence-guided geometric modeling to achieve stable and reproducible measurements. A three-branch local-structure-enhanced backbone improves the delineation of the pubic symphysis (PS) and fetal head (FH), while confidence-weighted contour fitting explicitly suppresses the influence of unreliable boundary points in AoP computation. To further improve performance under heterogeneous acquisition conditions, we introduce a lightweight geometry-reliable test-time adaptation strategy as an auxiliary component, enabling stable inference without target annotations. Extensive evaluations on multi-center benchmarks demonstrate consistent reductions in AoP error and boundary metrics compared with state-of-the-art AoP methods. Our source code is available at https://github.com/baiyou1234/R2AoP.

Yuanhan Wang, Yifei Chen, Beining Wu +7

Tsinghua University, Beijing, China · Hangzhou Dianzi University, Hangzhou, China · Shenzhen Research Institute of Big Data, Shenzhen, China

Ultrasound Image SegmentationUltrasound Imaging
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May 19, 2026cs.CV

Synergistic Foundation Models for Semi-Supervised Fetal Cardiac Ultrasound Analysis: SAM-Med2D Boundary Refinement and DINOv3 Semantic Enhancement

We present a semi-supervised framework for joint segmentation and classification of fetal cardiac ultrasound images. Built upon the EchoCare multi-task backbone, our method integrates SAM-Med2D for boundary refinement and leverages DINOv3 to enhance pseudo-label quality. We introduce view-specific hard masking along with a two-stage optimization strategy: an EMA phase to consolidate segmentation capabilities, followed by a Classification Fine-Tuning phase that freezes segmentation parameters and resets the classification head to recover classification performance without compromising segmentation gains. Evaluated on the FETUS 2026 leaderboard, our method achieves a Dice Similarity Coefficient at 79.99%, Normalized Surface Distance at 61.62%, and F1-score at 41.20%, validating the effectiveness of our approach for prenatal congenital heart disease screening. Source code is publicly available at: https://github.com/2826056177/zcst_fetus2026.

Tonghao Zhuang, Shanglong Hu, Yongsheng Luo +2

Zhuhai College of Science and Technology, Zhuhai, China

Ultrasound Image SegmentationMedical Imaging Foundation ModelsMedical Image ClassificationUltrasound ImagingSemi-Supervised Learning
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May 18, 2026cs.CV

Rad-VLSM: A Cross-Modal Framework with Semantics-Assisted Prompting for Medical Segmentation and Diagnosis

Medical image segmentation is more clinically valuable when it supports diagnosis rather than merely producing lesion masks. However, diagnostically relevant lesion cues are often subtle and localized, while existing models may be distracted by background tissues, acoustic artifacts, and irrelevant visual correlations. To address this problem, we propose Rad-VLSM, a two-stage cross-modal framework for semantics-assisted lesion focusing, robust segmentation, and visually grounded diagnosis. In the first stage, a BLIP-2-based vision-language alignment module identifies lesion-related candidate regions under semantic guidance and converts them into box prompts. In the second stage, these prompts are fed into a SAM-based multitask network, where a multi-candidate region aggregation strategy improves prompt stability and guides lesion segmentation. The predicted masks are then used as spatial priors for diagnosis, and a visual-radiomics fusion head integrates lesion-aware visual features with selected radiomics descriptors. By using semantic information for localization rather than direct prediction, Rad-VLSM reduces text-to-diagnosis dependence and grounds diagnosis in lesion-level evidence. Experiments on a private clinical breast ultrasound dataset and public benchmarks show that Rad-VLSM achieves strong segmentation and diagnostic performance with favorable generalization.

Fengyi Zhang, Xujie Zeng, Mohan Liu +2

Ultrasound Image SegmentationCross-Modality Medical Image SegmentationImage SegmentationText-Guided Image SegmentationPromptable Image SegmentationMedical Image Analysis
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May 14, 2026cs.CV

EAGT: Echocardiography Augmentation for Generalisability and Transferability

Deep learning models for echocardiography segmentation often struggle to generalise across institutions, scanners, and patient populations, where collecting large, consistently annotated datasets is infeasible. Data augmentation is inexpensive and widely used to improve the robustness of deep learning models; however, its role in enhancing cross-dataset generalisability in echocardiography remains insufficiently understood. This study presents a large-scale multi-dataset evaluation of 29 data augmentation techniques and their pairwise combinations for 2D left ventricular segmentation using a U-Net trained on Unity, CAMUS, and EchoNet Dynamic datasets. Each augmentation was explored under several hyperparameter settings and assessed through repeated runs using Dice and IoU in both in-domain and cross-dataset scenarios, with statistical significance quantified via independent t-tests. In-domain accuracy was near-saturated and insensitive to augmentation, whereas cross-dataset performance varied widely. Geometry-based augmentations including affine, shift-scale-rotate, flip, and perspective produced the largest and most consistent gains, while aggressive intensity- and artefact-based transforms often degraded transfer. Moreover, pairwise combinations outperformed individual augmentations mainly when the two transformations were complementary, particularly by improving some difficult domain-shift cases from poor to acceptable performance. These findings provide empirical guidance for designing augmentation policies that improve the robustness and transferability of echocardiography segmentation models.

Soroush Elyasi, Sara Adibzadeh, Nasim Dadashi Serej +1

THRIVE Centre, University of West London, London, United Kingdom

Ultrasound Image SegmentationData AugmentationEchocardiographyCross-Dataset Generalization
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May 14, 2026cs.CV

A CUBS-Compatible Ultrasound Morphology and Uncertainty-Aware Baseline for Carotid Intima-Media Segmentation and Preliminary Risk Prediction

Carotid atherosclerosis is a major contributor to ischemic stroke and transient ischemic attack. Conventional ultrasound assessment is commonly based on intima-media thickness, plaque appearance, stenosis degree, and peak systolic velocity, but these morphology- and velocity-based indicators may not fully capture patient-specific vascular risk. This study presents AtheroFlow-XNet, a CUBS-compatible ultrasound morphology and uncertainty-aware learning baseline for carotid intima-media segmentation and preliminary risk prediction. Using the Carotid Ultrasound Boundary Study dataset, manual lumen-intima and media-adventitia boundary annotations were converted into dense intima-media masks for supervised segmentation. Clinical variables were incorporated into an auxiliary risk-prediction branch, and Monte Carlo dropout was used for uncertainty-aware inference. The model was evaluated using a patient-level train-validation-test split with 1,522 training images, 326 validation images, and 328 testing images. The proposed model achieved a Dice coefficient of 0.7930 for LI-MA mask segmentation, a segmentation loss of 0.2359, and an area under the receiver operating characteristic curve of 0.6910 for preliminary risk prediction. Qualitative results showed that predicted masks were generally aligned with manual annotations, while uncertainty maps highlighted ambiguous wall-boundary regions. These results suggest that ultrasound-derived carotid morphology can support automated wall analysis and uncertainty-aware interpretation. Since CUBS does not provide Doppler waveforms or CFD-derived hemodynamic biomarkers, this work should be interpreted as a reproducible morphology-driven baseline. Future work will incorporate Doppler-derived flow profiles, patient-specific vascular reconstruction, and CFD-based wall shear biomarkers.

Aueaphum Aueawatthanaphisut

School of Information, Computer, and Communication Technology Sirindhorn International Institute of Technology, Thammasat University Pathum Thani, Thailand

Ultrasound Image SegmentationClinical Risk Prediction
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May 4, 2026cs.CV

Learning to Segment using Summary Statistics and Weak Supervision

Medical experts often manually segment images to obtain diagnostic statistics and discard the resulting annotations. We aim to train segmentation models to alleviate this burden, but constrained to the retained summary statistics (e.g., the area of the annotated region). Empirical results suggest that statistics alone are insufficient for this task, but adding weak information in the form of a few pixels within the area of interest significantly improves performance. We use a novel loss function that combines terms for image reconstruction quality, matching to summary statistics, and overlap between the predicted foreground and the weak supervisory signal. Experiments on standard image, ultrasound (breast cancer), and Computed Tomography (CT) scan (kidney tumors) data demonstrate the utility and potential of the approach.

Omkar Kulkarni, Edward Raff, Tim Oates

University of Maryland, Baltimore County, Baltimore MD, USA · Crowdstrike, Austin TX, USA

Ultrasound Image SegmentationImage SegmentationWeakly Supervised Image SegmentationWeakly Supervised LearningMedical Image Segmentation
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Apr 24, 2026cs.LG

Protect the Brain When Treating the Heart: A Convolutional Neural Network for Detecting Emboli

Gaseous microemboli (GME) represent a common complication of cardiac structural interventions across both surgical and transcatheter approaches. Transthoracic cardiac ultrasound imaging represents a convenient methodology to visualize the presence of circulating GME. However, their detection and quantification are far from trivial due to operator-dependent view, high velocity, and objects with similar structure in the background. Here, we propose an approach based on a 2.5D U-Net architecture to segment GME in space-time connected data. Such an approach yields robust detection against the background and high segmentation accuracy while retaining real-time execution speed. These properties facilitated the integration of the proposed pipeline into patient-monitoring surgical protocols, providing the quantification of GME area over time.

Andrea Angino, Ken Trotti, Diego Ulisse Pizzagalli +3

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

Ultrasound Image SegmentationImage SegmentationU-NetEchocardiographyUltrasound ImagingMedical Image Segmentation+1
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Apr 20, 2026cs.CV

Attention-ResUNet for Automated Fetal Head Segmentation

Automated fetal head segmentation in ultrasound images is critical for accurate biometric measurements in prenatal care. While existing deep learning approaches have achieved a reasonable performance, they struggle with issues like low contrast, noise, and complex anatomical boundaries which are inherent to ultrasound imaging. This paper presents Attention-ResUNet. It is a novel architecture that synergistically combines residual learning with multi-scale attention mechanisms in order to achieve enhanced fetal head segmentation. Our approach integrates attention gates at four decoder levels to focus selectively on anatomically relevant regions while suppressing the background noise, and complemented by residual connections which facilitates gradient flow and feature reuse. Extensive evaluation on the HC18 Challenge dataset where n = 200 demonstrates that Attention ResUNet achieves a superior performance with a mean Dice score of 99.30 +/- 0.14% against similar architectures. It significantly outperforms five baseline architectures including ResUNet (99.26%), Attention U-Net (98.79%), Swin U-Net (98.60%), Standard U-Net (98.58%), and U-Net++ (97.46%). Through statistical analysis we confirm highly significant improvements (p < 0.001) with effect sizes that range from 0.230 to 13.159 (Cohen's d). Using Saliency map analysis, we reveal that our architecture produces highly concentrated, anatomically consistent activation patterns, which demonstrate an enhanced interpretability which is crucial for clinical deployment. The proposed method establishes a new state of the art performance for automated fetal head segmentation whilst maintaining computational efficiency with 14.7M parameters and a 45 GFLOPs inference cost. Code repository: https://github.com/Ammar-ss

Ammar Bhilwarawala, Mainak Bandyopadhyay

School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India

Ultrasound Image SegmentationMedical Image SegmentationAttention Mechanisms
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Mar 1, 2026cs.CV

Adaptive Bidirectional Task Interaction for Joint Segmentation and Classification of Breast Ultrasound

Joint lesion segmentation and tissue classification in breast ultrasound are usually trained with a shared encoder, so the two branches stop exchanging information once their decoders separate. That is exactly where boundary detail and semantic evidence are most complementary. The proposed method restores this exchange during decoding and, because its value differs between images, lets the network decide per image how much to keep. A Task Interaction Module (TIM) at each of four decoder levels passes pooled boundary context into the classification representation and modulates decoder channels with class-conditioned priors. An Adaptive Interaction Weighting (AIW) unit then blends interacted and original features with a coefficient computed for each image and level. On BUSI the model reaches 74.19% IoU and 90.60% accuracy, and on BUSI-WHU 86.40% IoU and 95.00% accuracy, ahead of encoder-sharing multi-task, transformer segmentation and decoder-interaction baselines evaluated under the same protocol. The ablation shows that multi-scale context and cross-task exchange are not independent: applied separately they contribute 4.00 points of IoU in total, applied together 6.76. Adding the adaptive blend to task interaction alone raises AUC from 94.41% to 97.31%, indicating that the blend acts primarily on the classification branch. Code: https://github.com/C-loud-Nine/Adaptive-Task-Interaction-BUS.

Abdullah Al Shafi, Md Kawsar Mahmud Khan Zunayed, Safin Ahmmed +2

Khulna University of Engineering and Technology, Bangladesh · University Clermont Auvergne, France

Ultrasound Image SegmentationImage SegmentationMulti-Task LearningMedical Image ClassificationUltrasound Imaging
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