Breast and thyroid cancers are among the most prevalent and fastest growing malignancies worldwide with ultrasound imaging serving as the primary modality for screening and surgical navigation of these lesions. Accurate and real-time lesion segmentation in ultrasound images is crucial for guiding precise needle placement during biopsies and surgeries. To address this clinical need, we propose <b>MLP-UNet</b>, a deep learning model for automatic segmentation of breast tumors and thyroid nodules in ultrasound images. MLP-UNet adopts an encoder-decoder architecture with a U-shaped structure and integrates a MLP-based module(MAP) module within the encoder stage. Attention module is a lightweight employed during the skip connections to enhance feature representation. Using only using 33.75 M parameters, MLP-UNet achieves state-of-the-art segmentation performance. On the BUSI, it attains Dice, IoU, and Recall of 80.61%, 67.93%, and 80.48%, respectively. And on the DDTI, it attains Dice, IoU, and Recall of 81.67% for Dice, 71.72%. These results outperform several classical and state-of-the-art segmentation networks while maintaining low computational complexity, highlighting its significant potential for clinical application in ultrasound-guided surgical navigation systems.

We propose and evaluate multimodal deep learning (DL) approaches that combine ultrasound (US) B-mode and Nakagami parametric images for breast tumor classification. It is hypothesized that integrating tissue brightness information from B-mode images with scattering properties from Nakagami images will enhance diagnostic performance compared with single-input approaches. An EfficientNetV2B0 network was used to develop multimodal DL frameworks that took as input (i) numerical two-dimensional (2D) maps or (ii) rendered red-green-blue (RGB) representations of both B-mode and Nakagami data. The diagnostic performance of these frameworks was compared with single-input counterparts using 831 US acquisitions from 264 patients. In addition, gradient-weighted class activation mapping was applied to evaluate diagnostically relevant information utilized by the different networks. The multimodal architectures demonstrated significantly higher area under the receiver operating characteristic curve (AUC) values ( <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>p</mi> <mo><</mo> <mn>0.05</mn></mrow> </math> ) than their monomodal counterparts, achieving an average improvement of 10.75%. In addition, the multimodal networks incorporated, on average, 15.70% more diagnostically relevant tissue information. Among the multimodal models, those using RGB representations as input outperformed those that utilized 2D numerical data maps ( <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>p</mi> <mo><</mo> <mn>0.05</mn></mrow> </math> ). The top-performing multimodal architecture achieved a mean AUC of 0.896 [95% confidence interval (CI): 0.813 to 0.959] when performance was assessed at the image level and 0.848 (95% CI: 0.755 to 0.903) when assessed at the lesion level. Incorporating B-mode and Nakagami information together in a multimodal DL framework improved classification outcomes and increased the amount of diagnostically relevant information accessed by networks, highlighting the potential for automating and standardizing US breast cancer diagnostics to enhance clinical outcomes.

Four-dimensional (4D) ultrasound imaging is widely used in clinics for diagnostics and therapy guidance. Accurate target tracking in 4D ultrasound is crucial for autonomous therapy guidance systems, such as radiotherapy, where precise tumor localization ensures effective treatment. Supervised deep learning approaches rely on reliable ground truth, making accurate labels essential. We investigate the reliability of expert-labeled ground truth data by evaluating intra- and inter-observer variability in landmark labeling for 4D ultrasound imaging in the liver. Eight 4D liver ultrasound sequences were labeled by eight expert observers, each labeling eight landmarks three times. Intra- and inter-observer variability was quantified, and observer survey and motion analysis were conducted to determine factors influencing labeling accuracy, such as ultrasound artifacts and motion amplitude. The mean intra-observer variability ranged from <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mn>1.58</mn> <mtext>  </mtext> <mi>mm</mi> <mo>±</mo> <mn>0.90</mn> <mtext>  </mtext> <mi>mm</mi></mrow> </math> to <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mn>2.05</mn> <mtext>  </mtext> <mi>mm</mi> <mo>±</mo> <mn>1.22</mn> <mtext>  </mtext> <mi>mm</mi></mrow> </math> depending on the observer. The inter-observer variability for the two observer groups was <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mn>2.68</mn> <mtext>  </mtext> <mi>mm</mi> <mo>±</mo> <mn>1.69</mn> <mtext>  </mtext> <mi>mm</mi></mrow> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mn>3.06</mn> <mtext>  </mtext> <mi>mm</mi> <mo>±</mo> <mn>1.74</mn> <mtext>  </mtext> <mi>mm</mi></mrow> </math> . The observer survey and motion analysis revealed that ultrasound artifacts significantly affected labeling accuracy due to limited landmark visibility, whereas motion amplitude had no measurable effect. Our measured mean landmark motion was <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mn>11.56</mn> <mtext>  </mtext> <mi>mm</mi> <mo>±</mo> <mn>5.86</mn> <mtext>  </mtext> <mi>mm</mi></mrow> </math> . We highlight variability in expert-labeled ground truth data for 4D ultrasound imaging and identify ultrasound artifacts as a major source of labeling inaccuracies. These findings underscore the importance of addressing observer variability and artifact-related challenges to improve the reliability of ground truth data for evaluating target tracking algorithms in 4D ultrasound applications.

Ultrasound imaging is critical for clinical diagnostics, providing insights into various diseases and organs. However, artificial intelligence (AI) in this field faces challenges, such as the need for large labeled datasets and limited task-specific model applicability, particularly due to ultrasound's low signal-to-noise ratio (SNR). To overcome these, we introduce the Ultrasound Representation Foundation Model (URFM), designed to learn robust, generalizable representations from unlabeled ultrasound images, enabling label-efficient adaptation to diverse diagnostic tasks. URFM is pre-trained on over 1M images from 15 major anatomical organs using representation-based masked image modeling (MIM), an advanced self-supervised learning. Unlike traditional pixel-based MIM, URFM integrates high-level representations from BiomedCLIP, a specialized medical vision-language model, to address the low SNR issue. Extensive evaluation shows that URFM outperforms state-of-the-art methods, offering enhanced generalization, label efficiency, and training-time efficiency. URFM's scalability and flexibility signal a significant advancement in diagnostic accuracy and clinical workflow optimization in ultrasound imaging.

Thyroid cancer is one of the most common endocrine malignancies. Its incidence has steadily increased in recent years. Distinguishing between benign and malignant thyroid nodules (TNs) is challenging due to their overlapping imaging features. The rapid advancement of artificial intelligence (AI) in medical image analysis, particularly deep learning (DL) algorithms, has provided novel solutions for automated TN detection. However, existing studies exhibit substantial heterogeneity in diagnostic performance. Furthermore, no systematic evidence-based research comprehensively assesses the diagnostic performance of DL models in this field. This study aimed to execute a systematic review and meta-analysis to appraise the performance of DL algorithms in diagnosing TN malignancy, identify key factors influencing their diagnostic efficacy, and compare their accuracy with that of clinicians in image-based diagnosis. We systematically searched multiple databases, including PubMed, Cochrane, Embase, Web of Science, and IEEE, and identified 41 eligible studies for systematic review and meta-analysis. Based on the task type, studies were categorized into segmentation (n=14) and detection (n=27) tasks. The pooled sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) were calculated for each group. Subgroup analyses were performed to examine the impact of transfer learning and compare model performance against clinicians. For segmentation tasks, the pooled sensitivity, specificity, and AUC were 82% (95% CI 79%-84%), 95% (95% CI 92%-96%), and 0.91 (95% CI 0.89-0.94), respectively. For detection tasks, the pooled sensitivity, specificity, and AUC were 91% (95% CI 89%-93%), 89% (95% CI 86%-91%), and 0.96 (95% CI 0.93-0.97), respectively. Some studies demonstrated that DL models could achieve diagnostic performance comparable with, or even exceeding, that of clinicians in certain scenarios. The application of transfer learning contributed to improved model performance. DL algorithms exhibit promising diagnostic accuracy in TN imaging, highlighting their potential as auxiliary diagnostic tools. However, current studies are limited by suboptimal methodological design, inconsistent image quality across datasets, and insufficient external validation, which may introduce bias. Future research should enhance methodological standardization, improve model interpretability, and promote transparent reporting to facilitate the sustainable clinical translation of DL-based solutions.

Ultrasound imaging systems rely on accurate point spread function (PSF) estimation to support advanced image quality enhancement techniques such as deconvolution and speckle reduction. Phase aberration, caused by sound speed inhomogeneity within biological tissue, is inevitable in ultrasound imaging. It distorts the PSF by increasing sidelobe level and introducing asymmetric amplitude, making PSF estimation under phase aberration highly challenging. In this work, we propose a deep learning framework for estimating phase-aberrated PSFs using U-Net and complex U-Net architectures, operating on RF and complex k-space data, respectively, with the latter demonstrating superior performance. Synthetic phase aberration data, generated using the near-field phase screen model, is employed to train the networks. We evaluate various loss functions and find that log-compressed B-mode perceptual loss achieves the best performance, accurately predicting both the mainlobe and near sidelobe regions of the PSF. Simulation results validate the effectiveness of our approach in estimating PSFs under varying levels of phase aberration. Furthermore, we demonstrate that more accurate PSF estimation improves performance in a downstream phase aberration correction task, highlighting the broader utility of the proposed method.

To establish an ultrasound-based radiomics model to differentiate fibro adipose vascular anomaly (FAVA) and intramuscular venous malformation (VM). The clinical data of 65 patients with VM and 31 patients with FAVA who were treated and pathologically confirmed were retrospectively analyzed. Dimensionality reduction was performed on these features using the least absolute shrinkage and selection operator (LASSO). An ultrasound-based radiomics model was established using support vector machine (SVM) and random forest (RF) models. The diagnostic efficiency of this model was evaluated using the receiver operating characteristic. A total of 851 features were obtained by feature extraction, and 311 features were screened out using the <i>t</i>-test and Mann-Whitney <i>U</i> test. The dimensionality reduction was performed on the remaining features using LASSO. Finally, seven features were included to establish the diagnostic prediction model. In the testing group, the AUC, accuracy and specificity of the SVM model were higher than those of the RF model (0.841 [0.815-0.867] vs. 0.791 [0.759-0.824], 96.6% vs. 93.1%, and 100.0% vs. 90.5%, respectively). However, the sensitivity of the SVM model was lower than that of the RF model (88.9% vs. 100.0%). In this study, a prediction model based on ultrasound radiomics was developed to distinguish FAVA from VM. The study achieved high classification accuracy, sensitivity, and specificity. SVM model is superior to RF model and provides a new perspective and tool for clinical diagnosis.

Purpose: Prenatal ultrasound is a key tool in evaluating fetal structural development and detecting abnormalities, contributing to reduced perinatal complications and improved neonatal survival. Accurate identification of standard fetal torso planes is essential for reliable assessment and personalized prenatal care. However, limitations such as low contrast and unclear texture details in ultrasound imaging pose significant challenges for fine-grained anatomical recognition. Methods: We propose a novel Multi-Contrast Fusion Module (MCFM) to enhance the model's ability to extract detailed information from ultrasound images. MCFM operates exclusively on the lower layers of the neural network, directly processing raw ultrasound data. By assigning attention weights to image representations under different contrast conditions, the module enhances feature modeling while explicitly maintaining minimal parameter overhead. Results: The proposed MCFM was evaluated on a curated dataset of fetal torso plane ultrasound images. Experimental results demonstrate that MCFM substantially improves recognition performance, with a minimal increase in model complexity. The integration of multi-contrast attention enables the model to better capture subtle anatomical structures, contributing to higher classification accuracy and clinical reliability. Conclusions: Our method provides an effective solution for improving fetal torso plane recognition in ultrasound imaging. By enhancing feature representation through multi-contrast fusion, the proposed approach supports clinicians in achieving more accurate and consistent diagnoses, demonstrating strong potential for clinical adoption in prenatal screening. The codes are available at https://github.com/sysll/MCFM.

<b>Objective:</b> To develop a machine learning diagnostic model for T-shaped uterus based on quantitative parameters from 3D transvaginal ultrasound. <b>Methods:</b> A retrospective cross-sectional study was conducted, recruiting 304 patients who visited the hysteroscopy centre of Fuxing Hospital, Beijing, China, between July 2021 and June 2024 for reasons such as "infertility or recurrent pregnancy loss" and other adverse obstetric histories. Twelve experts, including seven clinicians and five sonographers, from Fuxing Hospital and Beijing Obstetrics and Gynecology Hospital of Capital Medical University, Peking University People's Hospital, and Beijing Hospital, independently and anonymously assessed the diagnosis of T-shaped uterus using a modified Delphi method. Based on the consensus results, 56 cases were classified into the T-shaped uterus group and 248 cases into the non-T-shaped uterus group. A total of 7 clinical features and 14 sonographic features were initially included. Features demonstrating significant diagnostic impact were selected using 10-fold cross-validated LASSO (Least Absolute Shrinkage and Selection Operator) regression. Four machine learning algorithms [logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM)] were subsequently implemented to develop T-shaped uterus diagnostic models. Using the Python random module, the patient dataset was randomly divided into five subsets, each maintaining the original class distribution (T-shaped uterus: non-T-shaped uterus ≈ 1∶4) and a balanced number of samples between the two categories. Five-fold cross-validation was performed, with four subsets used for training and one for validation in each round, to enhance the reliability of model evaluation. Model performance was rigorously assessed using established metrics: area under the curve (AUC) of receiver operator characteristic (ROC) curve, sensitivity, specificity, precision, and F1-score. In the RF model, feature importance was assessed by the mean decrease in Gini impurity attributed to each variable. <b>Results:</b> A total of 304 patients had a mean age of (35±4) years, and the age of the T-shaped uterus group was (35±5) years; the age of the non-T-shaped uterus group was (34±4) years.. Eight features with non-zero coefficients were selected by LASSO regression, including average lateral wall indentation width, average lateral wall indentation angle, upper cavity depth, endometrial thickness, uterine cavity area, cavity width at level of lateral wall indentation, angle formed by the bilateral lateral walls, and average cornual angle (coefficient: 0.125, -0.064,-0.037,-0.030,-0.026,-0.025,-0.025 and -0.024, respectively). The RF model showed the best diagnostic performance: in training set, AUC was 0.986 (95%<i>CI</i>: 0.980-0.992), sensitivity was 0.978, specificity 0.946, precision 0.802, and F1-score 0.881; in testing set, AUC was 0.948 (95%<i>CI</i>: 0.911-0.985), sensitivity was 0.873, specificity 0.919, precision 0.716, and F1-score 0.784. RF model feature importance analysis revealed that average lateral wall indentation width, upper cavity depth, and average lateral wall indentation angle were the top three features (over 65% in total), playing a decisive role in model prediction. <b>Conclusion:</b> The machine learning models developed in this study, particularly the RF model, are promising for the diagnosis of T-shaped uterus, offering new perspectives and technical support for clinical practice.

Accurate segmentation of thyroid nodules in ultrasound images is critical for clinical diagnosis but remains challenging due to low contrast and complex anatomical structures. Existing deep learning methods often rely solely on local nodule features, lacking anatomical prior knowledge of the thyroid region, which can result in misclassification of non-thyroid tissues, especially in low-quality scans. To address these issues, we propose a Spatial Prior-Guided Dual-Path Network that integrates a prior-aware encoder to model thyroid anatomical structures and a low-cost heterogeneous encoder to preserve fine-grained multi-scale features, enhancing both spatial detail and contextual awareness. To capture the diverse and irregular appearances of nodules, we design a CrossBlock module, which combines an efficient cross-attention mechanism with mixed-scale convolutional operations to enable global context modeling and local feature extraction. The network further employs a dual-decoder architecture, where one decoder learns thyroid region priors and the other focuses on accurate nodule segmentation. Gland-specific features are hierarchically refined and injected into the nodule decoder to enhance boundary delineation through anatomical guidance. Extensive experiments on the TN3K and MTNS datasets demonstrate that our method consistently outperforms state-of-the-art approaches, particularly in boundary precision and localization accuracy, offering practical value for preoperative planning and clinical decision-making.