Ultrasonic Imaging最新文献

{"title":"Automated Deep Learning-Based Finger Joint Segmentation in 3-D Ultrasound Images With Limited Dataset.","authors":"Grigorios M Karageorgos,Jianwei Qiu,Xiaorui Peng,Zhaoyuan Yang,Soumya Ghose,Aaron Dentinger,Zhanpeng Xu,Janggun Jo,Siddarth Ragupathi,Guan Xu,Nada Abdulaziz,Girish Gandikota,Xueding Wang,David Mills","doi":"10.1177/01617346241277178","DOIUrl":"https://doi.org/10.1177/01617346241277178","url":null,"abstract":"Ultrasound imaging has shown promise in assessing synovium inflammation associated early stages of rheumatoid arthritis (RA). The precise identification of the synovium and the quantification of inflammation-specific imaging biomarkers is a crucial aspect of accurately quantifying and grading RA. In this study, a deep learning-based approach is presented that automates the segmentation of the synovium in ultrasound images of finger joints affected by RA. Two convolutional neural network architectures for image segmentation were trained and validated in a limited number of 2-D images, extracted from N = 18 3-D ultrasound volumes acquired from N = 9 RA patients, with sparse ground truth annotations of the synovium. Various augmentation strategies were employed to enhance the diversity and size of the training dataset. The utilization of geometric and noise augmentation transforms resulted in the highest dice score (0.768 ±0.031,N=6),andintersectionoverunion(0.624±0.040, N = 6), as determined via six-fold cross-validation. In addition, the segmentation model is used to generate dense 3-D segmentation maps in the ultrasound volumes, based on the available sparse annotations. The developed technique shows promise in facilitating more efficient and standardized workflow for RA screening using ultrasound imaging.","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":"18 1","pages":"1617346241277178"},"PeriodicalIF":2.3,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142262535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CBAM-RIUnet: Breast Tumor Segmentation With Enhanced Breast Ultrasound and Test-Time Augmentation","authors":"Lal Omega Boro, Gypsy Nandi","doi":"10.1177/01617346241276411","DOIUrl":"https://doi.org/10.1177/01617346241276411","url":null,"abstract":"This study addresses the challenge of precise breast tumor segmentation in ultrasound images, crucial for effective Computer-Aided Diagnosis (CAD) in breast cancer. We introduce CBAM-RIUnet, a deep learning (DL) model for automated breast tumor segmentation in breast ultrasound (BUS) images. The model, featuring an efficient convolutional block attention module residual inception Unet, outperforms existing models, particularly excelling in Dice and IoU scores. CBAM-RIUnet follows the Unet structure with a residual inception depth-wise separable convolution, and incorporates a convolutional block attention module (CBAM) to eliminate irrelevant features and focus on the region of interest. Evaluation under three scenarios, including enhanced breast ultrasound (EBUS) and test-time augmentation (TTA), demonstrates impressive results. CBAM-RIUnet achieves Dice and IoU scores of 89.38% and 88.71%, respectively, showcasing significant improvements compared to state-of-the-art DL techniques. In conclusion, CBAM-RIUnet presents a highly effective and simplified DL model for breast tumor segmentation in BUS imaging.","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":"3 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142262534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning Radiomics Based on Two-Dimensional Ultrasound for Predicting the Efficacy of Neoadjuvant Chemotherapy in Breast Cancer","authors":"Zhan Wang, Xiaoqin Li, Heng Zhang, Tongtong Duan, Chao Zhang, Tong Zhao","doi":"10.1177/01617346241276168","DOIUrl":"https://doi.org/10.1177/01617346241276168","url":null,"abstract":"We investigate the predictive value of a comprehensive model based on preoperative ultrasound radiomics, deep learning, and clinical features for pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for the breast cancer. We enrolled 155 patients with pathologically confirmed breast cancer who underwent NAC. The patients were randomly divided into the training set and the validation set in the ratio of 7:3. The deep learning and radiomics features of pre-treatment ultrasound images were extracted, and the random forest recursive elimination algorithm and the least absolute shrinkage and selection operator were used for feature screening and DL-Score and Rad-Score construction. According to multifactorial logistic regression, independent clinical predictors, DL-Score, and Rad-Score were selected to construct the comprehensive prediction model DLRC. The performance of the model was evaluated in terms of its predictive effect, and clinical practicability. Compared to the clinical, radiomics (Rad-Score), and deep learning (DL-Score) models, the DLRC accurately predicted the pCR status, with an area under the curve (AUC) of 0.937 (95%CI: 0.895–0.970) in the training set and 0.914 (95%CI: 0.838–0.973) in the validation set. Moreover, decision curve analysis confirmed that the DLRC had the highest clinical value among all models. The comprehensive model DLRC based on ultrasound radiomics, deep learning, and clinical features can effectively and accurately predict the pCR status of breast cancer after NAC, which is conducive to assisting clinical personalized diagnosis and treatment plan.","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":"68 1","pages":"1617346241276168"},"PeriodicalIF":2.3,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SPGAN Optimized by Piranha Foraging Optimization for Thyroid Nodule Classification in Ultrasound Images","authors":"Siddalingesh Bandi, Ravikumar K.P, Manjunatha Reddy H.S","doi":"10.1177/01617346241271240","DOIUrl":"https://doi.org/10.1177/01617346241271240","url":null,"abstract":"In this research work, Semantic-Preserved Generative Adversarial Network optimized by Piranha Foraging Optimization for Thyroid Nodule Classification in Ultrasound Images (SPGAN-PFO-TNC-UI) is proposed. Initially, ultrasound images are gathered from the DDTI dataset. Then the input image is sent to the pre-processing step. During pre-processing stage, the Multi-Window Savitzky-Golay Filter (MWSGF) is employed to reduce the noise and improve the quality of the ultrasound (US) images. The pre-processed output is supplied to the Generalized Intuitionistic Fuzzy C-Means Clustering (GIFCMC). Here, the ultrasound image’s Region of Interest (ROI) is segmented. The segmentation output is supplied to the Fully Numerical Laplace Transform (FNLT) to extract the features, such as geometric features like solidity, orientation, roundness, main axis length, minor axis length, bounding box, convex area, and morphological features, like area, perimeter, aspect ratio, and AP ratio. The Semantic-Preserved Generative Adversarial Network (SPGAN) separates the image as benign or malignant nodules. Generally, SPGAN does not express any optimization adaptation methodologies for determining the best parameters to ensure the accurate classification of thyroid nodules. Therefore, the Piranha Foraging Optimization (PFO) algorithm is proposed to improve the SPGAN classifier and accurately identify the thyroid nodules. The metrics, like F-score, accuracy, error rate, precision, sensitivity, specificity, ROC, computing time is examined. The proposed SPGAN-PFO-TNC-UI method attains 30.54%, 21.30%, 27.40%, and 18.92% higher precision and 26.97%, 20.41%, 15.09%, and 18.27% lower error rate compared with existing techniques, like Thyroid detection and classification using DNN with Hybrid Meta-Heuristic and LSTM (TD-DL-HMH-LSTM), Quantum-Inspired convolutional neural networks for optimized thyroid nodule categorization (QCNN-OTNC), Thyroid nodules classification under Follow the Regularized Leader Optimization based Deep Neural Networks (CTN-FRL-DNN), Automatic classification of ultrasound thyroids images using vision transformers and generative adversarial networks (ACUTI-VT-GAN) respectively.","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":"65 1","pages":"1617346241271240"},"PeriodicalIF":2.3,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Skeletal Muscle Texture Assessment Using Ultrasonography: Comparison with Magnetic Resonance Imaging in Chronic Kidney Disease.","authors":"Thomas J Wilkinson, Luke A Baker, Emma L Watson, Katerina Nikopoulou, Christina Karatzaferi, Matthew Pm Graham-Brown, Alice C Smith, Giorgos K Sakkas","doi":"10.1177/01617346241255879","DOIUrl":"10.1177/01617346241255879","url":null,"abstract":"<p><p>Skeletal muscle dysfunction is common in chronic kidney disease (CKD). Of interest is the concept of \"muscle quality,\" of which measures include ultrasound-derived echo intensity (EI). Alternative parameters of muscle texture, for example, gray level of co-occurrence matrix (GCLM), are available and may circumvent limitations in EI. The validity of EI is limited in humans, particularly in chronic diseases. This study aimed to investigate the associations between ultrasound-derived parameters of muscle texture with MRI. Images of the thigh were acquired using a 3 Tesla MRI scanner. Quantification of muscle (contractile), fat (non-contractile), and miscellaneous (connective tissue, fascia) components were estimated. Anatomical rectus femoris cross-sectional area was measured using B-mode 2D ultrasonography. To assess muscle texture, first (i.e., EI)- and second (i.e., GLCM)-order statistical analyses were performed. Fourteen participants with CKD were included (age: 58.0 ± 11.9 years, 50% male, eGFR: 27.0 ± 7.4 ml/min/1.73m², 55% Stage 4). Higher EI was associated with lower muscle % (quadriceps: β = -.568, <i>p</i> = .034; hamstrings: β = -.644, <i>p</i> = .010). Higher EI was associated with a higher fat % in the hamstrings (β = -.626, <i>p</i> = .017). A higher angular second moment from GLCM analysis was associated with greater muscle % (β = .570, <i>p</i> = .033) and lower fat % (β = -.534, <i>p</i> = .049). A higher inverse difference moment was associated with greater muscle % (β = .610, <i>p</i> = .021 and lower fat % (β = -.599, <i>p</i> = .024). This is the first study to investigate the associations between ultrasound-derived parameters of muscle texture with MRI. Our preliminary findings suggest ultrasound-derived texture analysis provides a novel indicator of reduced skeletal muscle % and thus increased intramuscular fat.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"263-268"},"PeriodicalIF":2.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11325600/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141162295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Elevation Resolution Enhancement Oriented 3D Ultrasound Imaging.","authors":"Kai Fan, Yunye Cai, Enxiang Shen, Yuxin Wang, Jie Yuan, Chao Tao, Xiaojun Liu","doi":"10.1177/01617346241259049","DOIUrl":"10.1177/01617346241259049","url":null,"abstract":"<p><p>Three-dimensional (3D) ultrasound imaging can be accomplished by reconstructing a sequence of two-dimensional (2D) ultrasound images. However, 2D ultrasound images usually suffer from low resolution in the elevation direction, thereby impacting the accuracy of 3D reconstructed results. The lateral resolution of 2D ultrasound is known to significantly exceed the elevation resolution. By combining scanning sequences acquired from orthogonal directions, the effects of poor elevation resolution can be mitigated through a composite reconstructing process. Moreover, capturing ultrasound images from multiple perspectives necessitates a precise probe positioning method with a wide angle of coverage. Optical tracking is popularly used for probe positioning for its high accuracy and environment-robustness. In this paper, a novel large-angle accurate optical positioning method is used for enhancing resolution in 3D ultrasound imaging through orthogonal-view scanning and composite reconstruction. Experiments on two phantoms proved that our method could significantly improve reconstruction accuracy in the elevation direction of the probe compared with single-angle parallel scanning. The results indicate that our method holds the potential to improve current 3D ultrasound imaging techniques.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"220-232"},"PeriodicalIF":2.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141433223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Scatterer Parameters From Ultrasound Scattering Models Taking Into Account Scattering From Nuclei and Cells of Cell-Pellet Biophantoms and Ex Vivo Tumors.","authors":"Pauline Muleki-Seya, William D O'Brien","doi":"10.1177/01617346241256120","DOIUrl":"10.1177/01617346241256120","url":null,"abstract":"<p><p>The Quantitative Ultrasound backscatter coefficient provides the capability to evaluate tissue microstructure parameters. Tissue-based scatterer parameters are extracted using ultrasound scattering models. It is challenging to correlate ultrasound scatterer parameters of tissue structures from optical-measured histology, possibly because of inappropriate scattering models or the presence of multiple scatterers. The objective of this study is to pursue the quantification of pertinent scatterer parameters with scattering models that consider ultrasound scattering from nuclei and cells. The concentric sphere model (CSM) and the structure factor model adapted for two types of scatterers (SFM2) are evaluated for cell-pellet biophantoms and ex vivo tumors of four cell lines: 4T1, JC, LMTK, and MAT. The structure factor model (SFM) was used for comparison. CSM and SFM2 provided scatterer parameters closer to histology (lower relative errors) for nucleus and cell radii and volume fractions than SFM but were not always accompanied by lower dispersion of the scatterer distribution (lower coefficient of variation). CSM and SFM2 quantified cell and nucleus radius and volume fraction parameters with lower relative error compared to SFM. For tumors, CSM provided better results than SFM2.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"233-250"},"PeriodicalIF":2.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141318747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Quantitative Assessment of Muscle Strength Based on Deep Learning and Ultrasound.","authors":"Xiao Yang, Beilei Zhang, Ying Liu, Qian Lv, Jianzhong Guo","doi":"10.1177/01617346241255590","DOIUrl":"10.1177/01617346241255590","url":null,"abstract":"<p><p>Skeletal muscle is a vital organ that promotes human movement and maintains posture. Accurate assessment of muscle strength is helpful to provide valuable insights for athletes' rehabilitation and strength training. However, traditional techniques rely heavily on the operator's expertise, which may affect the accuracy of the results. In this study, we propose an automated method to evaluate muscle strength using ultrasound and deep learning techniques. B-mode ultrasound data of biceps brachii of multiple athletes at different strength levels were collected and then used to train our deep learning model. To evaluate the effectiveness of this method, this study tested the contraction of the biceps brachii under different force levels. The classification accuracy of this method for grade 4 and grade 6 muscle strength reached 98% and 96%, respectively, and the overall average accuracy was 93% and 87%, respectively. The experimental results confirm that the innovative methods in this paper can accurately and effectively evaluate and classify muscle strength.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"211-219"},"PeriodicalIF":2.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D Ultrasound Shear Wave Elastography for Musculoskeletal Tissue Assessment Under Compressive Load: A Feasibility Study.","authors":"Bryan J Ranger, Kevin M Moerman, Micha Feigin, Hugh M Herr, Brian W Anthony","doi":"10.1177/01617346241253798","DOIUrl":"10.1177/01617346241253798","url":null,"abstract":"<p><p>Given its real-time capability to quantify mechanical tissue properties, ultrasound shear wave elastography holds significant promise in clinical musculoskeletal imaging. However, existing shear wave elastography methods fall short in enabling full-limb analysis of 3D anatomical structures under diverse loading conditions, and may introduce measurement bias due to sonographer-applied force on the transducer. These limitations pose numerous challenges, particularly for 3D computational biomechanical tissue modeling in areas like prosthetic socket design. In this feasibility study, a clinical linear ultrasound transducer system with integrated shear wave elastography capabilities was utilized to scan both a calibrated phantom and human limbs in a water tank imaging setup. By conducting 2D and 3D scans under varying compressive loads, this study demonstrates the feasibility of volumetric ultrasound shear wave elastography of human limbs. Our preliminary results showcase a potential method for evaluating 3D spatially varying tissue properties, offering future extensions to computational biomechanical modeling of tissue for various clinical scenarios.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"251-262"},"PeriodicalIF":2.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141071776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstracts for the 2024 Ultrasonic Imaging and Tissue Characterization Symposium (UITC)","authors":"","doi":"10.1177/01617346241257304","DOIUrl":"https://doi.org/10.1177/01617346241257304","url":null,"abstract":"","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":"44 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}