Analysis Of Ultrasound Images Research Articles

Deep learning for thyroid nodule examination: a technical review

In recent years, the incidence of thyroid cancer has increased dramatically, resulting in an increased demand for early thyroid nodule examination. Ultrasound (US) imaging is the modality most frequently used to image thyroid nodules; However, the low image resolution, speckle noise, and high variability make it difficult to utilize traditional image processing techniques. Recent advances in deep learning (DL) have increased research into the automated processing of thyroid US images. We review three main image processing tasks for thyroid nodule analysis: classification, segmentation, and detection. We discuss the advantages and limitations of the recently proposed DL techniques as well as the data availability and algorithmic efficacy. In addition, we investigate the remaining obstacles and future potential for automated analysis of thyroid US images.

Estimasi Kualitas Karkas Hidup pada Kerbau Rawa dan Kerbau Sungai dengan Ultrasonografi pada Berbagai Umur

Ultrasonography is a non-invasive diagnostic tool which potential for examining carcasses periodically, cheap, easy, and accurate to demonstrate carcass characteristics, quantity and quality of meat. This study aims to predict the quality of live carcass in swamp buffaloes and river buffaloes based on age and breed by ultrasonography. Ultrasound imaging was carried out with a longitudinal and transverse point of view using a convex transducer with a frequency of 5 MHz and a depth of 180 mm. There were 10 swamp buffaloes and 10 river buffaloes that were grouped into 1-3 years (n = 5) and 3-6 years (n = 5). The result of ultrasound images appears as anechoic images of muscle and hyperechoic images of fat and bone. Carcass analysis used AUSMEAT with the regression y = 0.4546+0.1697 x with R2 of 0.9833 for marbling score and the regression y = - 0.03003+0.4217 x with R2 of 0.9933 for percentage of intramuscular fat showed that swamp buffaloes and river buffaloes aged 3-6 years had the Longissimus dorsi muscle thickness, rump muscle thickness, backfat thickness, rump fat thickness, marbling score and percentage of intramuscular fat were higher than aged 1-3 years (P<0.05). However, analysis of ultrasound images based on breed showed that swamp buffaloes and river buffaloes had the same carcass quality (P>0.05).

Abstract A078: Response and resistance to KRAS inhibition in PDAC mouse models

Abstract Pancreatic ductal adenocarcinoma (PDAC), among the most lethal cancers with a 5-year survival rate of 11%, is a KRAS-driven disease. Studies have shown that a single glycine (G) to aspartic acid (D) substitution on codon 12 of KRAS protein (known as KRASG12D mutation) appears in 41% of all PDAC cases, highlighting the necessity of targeting KRASG12D as a therapeutic approach. MRTX1133, a recently developed potent, non-covalent, direct, and selective KRASG12D small molecule inhibitor demonstrates a high-affinity interaction with KRASG12D. Previous studies have shown striking efficacy of MRTX1133 on growth inhibition of mouse/human PDAC tumor cells carrying KRASG12D mutation in various in vivo and in vitro models, during short-term treatment. Here in our study, we investigated the efficacy of continuous long-term treatment with MRTX1133 in two different PDAC mouse models: KPC (a genetically engineered mouse model harboring a KRASG12D mutation) and its syngeneic orthotopic model. In both models, tumor sizes were detected by ultrasound imaging and after reaching 100-300 mm3 in volume, mice were enrolled in either MRTX1133 or vehicle treatment (30mg/kg, IP, BID). Tumor sizes were monitored by ultrasound regularly and the treatment continued until mice reached clinical endpoint. At endpoint, mice were sacrificed, and tumors were harvested for a series of downstream experiments. Our results showed that while, in both models, vehicle treated tumors grew aggressively and mice reached endpoint in average 6 days post treatment, drug treated tumors regressed rapidly and survival rate increased significantly to as late as 222 days post treatment in KPC model. However, in both models, drug treated tumors ultimately relapsed, developed resistance to the drug, and resulted in death. Further analysis of KPC mice ultrasound images evidenced that the relapsed tumors occurred at the same sites of the primary tumors. This suggests the possibility of relapsed tumors arising from the regrowth of the dormant residual cells from the primary tumors. Re-implantation of the resistant tumors either in the flank or pancreata of the new wildtype mice resulted in the formation of drug-resistant tumors in hosts. In addition, histopathology analysis of the tumor sections demonstrated a significant increase in the ratio of invasive PDAC to PanIN areas in the drug resistant tumors. Furthermore, multiplex immunofluorescence of both MRTX1133 resistant and vehicle treated tumors from KPC mice demonstrated co-expression of markers representing basal-like and classical PDAC subtypes. Moreover, flow cytometry analysis indicated an increase in the myeloid population of the resistant tumors comparing to the vehicle treated group. In the future directions, through applying a broad range of cellular and molecular experiments we will characterize the kinetics of KRAS inhibitor drug resistance and define possible combination therapy startegies to overcome drug resistance mechanisms. Citation Format: Laleh Abbassi, Jullien Dilly, Annan Yang, Giselle Uribe, Branden Parent, Connor Hennessey, Kevin Kapner, Alexander Jordan, Shatavisha Dasgupta, Micaela Morgado, Taimour Baslan, Li Qiang, Peter Winter, Seema Chugh, Alex Shalek, Ben Stanger, Srivastan Raghavan, Scott Lowe, Brian Wolpin, Stephanie Dougan, Jonathan Nowak, Andrew Aguirre. Response and resistance to KRAS inhibition in PDAC mouse models [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr A078.

UGGNet: Bridging U-Net and VGG for Advanced Breast Cancer Diagnosis

In the field of medical imaging, breast ultrasound has emerged as a crucial diagnostic tool for early detection of breast cancer. However, the accuracy of diagnosing the location of the affected area and the extent of the disease depends on the experience of the physician. In this paper, we propose a novel model called UGGNet, combining the power of the U-Net and VGG architectures to enhance the performance of breast ultrasound image analysis. The U-Net component of the model helps accurately segment the lesions, while the VGG component utilizes deep convolutional layers to extract features. The fusion of these two architectures in UGGNet aims to optimize both segmentation and feature representation, providing a comprehensive solution for accurate diagnosis in breast ultrasound images. Experimental results have demonstrated that the UGGNet model achieves a notable accuracy of 78.2\% on the "Breast Ultrasound Images Dataset."

In Situ Monitoring and Assessment of Ischemic Skin Flap by High-Frequency Ultrasound and Quantitative Parameters.

Skin flap surgery is a critical procedure for treating severe skin injury in which post-surgery lesions must well monitored and cared for noninvasively. In the present study, attempts using high-frequency ultrasound imaging, quantitative parameters, and statistical analysis were made to extensively assess variations in the skin flap. Experiments were arranged by incising the dorsal skin of rats to create a skin flap using the chamber model. Measurements, including photographs, 30 MHz ultrasound B-mode images, skin thickness, echogenicity, Nakagami statistics, and histological analysis of post-surgery skin flap, were performed. Photograph results showed that color variations in different parts of the skin flap may readily correspond to ischemic states of local tissues. Compared to post-surgery skin flap on day 7, both integrated backscatter (IB) and Nakagami parameter (m) of the distal part of tissues were increased, and those of the skin thickness were decreased. Overall, relative skin thickness, IB, and m of the distal part of post-surgery skin flap varied from 100 to 67%, -66 to -61 dB, and 0.48 to 0.36, respectively. These results demonstrate that this modality and quantitative parameters can be feasibly applied for long-term and in situ assessment of skin flap tissues.

Non-contrast ultrasound image analysis for spatial and temporal distribution of blood flow after spinal cord injury

Ultrasound technology can provide high-resolution imaging of blood flow following spinal cord injury (SCI). Blood flow imaging may improve critical care management of SCI, yet its duration is limited clinically by the amount of contrast agent injection required for high-resolution, continuous monitoring. In this study, we aim to establish non-contrast ultrasound as a clinically translatable imaging technique for spinal cord blood flow via comparison to contrast-based methods and by measuring the spatial distribution of blood flow after SCI. A rodent model of contusion SCI at the T12 spinal level was carried out using three different impact forces. We compared images of spinal cord blood flow taken using both non-contrast and contrast-enhanced ultrasound. Subsequently, we processed the images as a function of distance from injury, yielding the distribution of blood flow through space after SCI, and found the following. (1) Both non-contrast and contrast-enhanced imaging methods resulted in similar blood flow distributions (Spearman’s ρ = 0.55, p < 0.0001). (2) We found an area of decreased flow at the injury epicenter, or umbra (p < 0.0001). Unexpectedly, we found increased flow at the periphery, or penumbra (rostral, p < 0.05; caudal, p < 0.01), following SCI. However, distal flow remained unchanged, in what is presumably unaffected tissue. (3) Finally, tracking blood flow in the injury zones over time revealed interesting dynamic changes. After an initial decrease, blood flow in the penumbra increased during the first 10 min after injury, while blood flow in the umbra and distal tissue remained constant over time. These results demonstrate the viability of non-contrast ultrasound as a clinical monitoring tool. Furthermore, our surprising observations of increased flow in the injury periphery pose interesting new questions about how the spinal cord vasculature reacts to SCI, with potentially increased significance of the penumbra.

Convolutional Neural Network Approaches in Median Nerve Morphological Assessment from Ultrasound Images.

Ultrasound imaging has been used to investigate compression of the median nerve in carpal tunnel syndrome patients. Ultrasound imaging and the extraction of median nerve parameters from ultrasound images are crucial and are usually performed manually by experts. The manual annotation of ultrasound images relies on experience, and intra- and interrater reliability may vary among studies. In this study, two types of convolutional neural networks (CNNs), U-Net and SegNet, were used to extract the median nerve morphology. To the best of our knowledge, the application of these methods to ultrasound imaging of the median nerve has not yet been investigated. Spearman's correlation and Bland-Altman analyses were performed to investigate the correlation and agreement between manual annotation and CNN estimation, namely, the cross-sectional area, circumference, and diameter of the median nerve. The results showed that the intersection over union (IoU) of U-Net (0.717) was greater than that of SegNet (0.625). A few images in SegNet had an IoU below 0.6, decreasing the average IoU. In both models, the IoU decreased when the median nerve was elongated longitudinally with a blurred outline. The Bland-Altman analysis revealed that, in general, both the U-Net- and SegNet-estimated measurements showed 95% limits of agreement with manual annotation. These results show that these CNN models are promising tools for median nerve ultrasound imaging analysis.

Application of an artificial intelligence-based system in the diagnosis of breast ultrasound images obtained using a smartphone

BackgroundBreast ultrasound (US) is useful for dense breasts, and the introduction of artificial intelligence (AI)-assisted diagnoses of breast US images should be considered. However, the implementation of AI-based technologies in clinical practice is problematic because of the costs of introducing such approaches to hospital information systems (HISs) and the security risk of connecting HIS to the Internet to access AI services. To solve these problems, we developed a system that applies AI to the analysis of breast US images captured using a smartphone.MethodsTraining data were prepared using 115 images of benign lesions and 201 images of malignant lesions acquired at the Division of Breast Surgery, Gifu University Hospital. YOLOv3 (object detection models) was used to detect lesions on US images. A graphical user interface (GUI) was developed to predict an AI server. A smartphone application was also developed for capturing US images displayed on the HIS monitor with its camera and displaying the prediction results received from the AI server. The sensitivity and specificity of the prediction performed on the AI server and via the smartphone were calculated using 60 images spared from the training.ResultsThe established AI showed 100% sensitivity and 75% specificity for malignant lesions and took 0.2 s per prediction with the AI sever. Prediction using a smartphone required 2 s per prediction and showed 100% sensitivity and 97.5% specificity for malignant lesions.ConclusionsGood-quality predictions were obtained using the AI server. Moreover, the quality of the prediction via the smartphone was slightly better than that on the AI server, which can be safely and inexpensively introduced into HISs.

Benchmarking Supervised and Self-Supervised Learning Methods in A Large Ultrasound Muti-task Images Dataset.

Deep learning in ultrasound(US) imaging aims to construct foundational models that accurately reflect the modality's unique characteristics. Nevertheless, the limited datasets and narrow task types have restricted this field in recent years. To address these challenges, we introduce US-MTD120K, a multi-task ultrasound dataset with 120,354 real-world two-dimensional images. This dataset covers three standard plane recognition and two diagnostic tasks in ultrasound imaging, providing a rich basis for model training and evaluation. We detail the data collection, distribution, and labelling processes, ensuring a thorough understanding of the dataset's structure. Furthermore, we conduct extensive benchmark tests on 27 state-of-the-art methods from both supervised and self-supervised learning(SSL) perspectives. In the realm of supervised learning, we analyze the sensitivity of two main feature computation methods to ultrasound images at the representational level, highlighting that models which judiciously constrain global feature computation could potentially serve as a viable analytical approach for US image analysis. In the context of self-supervised learning, we delved into the modelling process of self-supervised learning models for medical images and proposed an improvement strategy, named MoCo-US, a solution that addresses the excessive reliance on pretext task design from the input side. It achieves competitive performance with minimal pretext task design and enhances other SSL methods simply. The dataset and the code will be available at https://github.com/JsongZhang/CDOA-for-UMTD.

Malignancy pattern analysis of breast ultrasound images using clinical features and a graph convolutional network.

Early diagnosis of breast cancer can lead to effective treatment, possibly increase long-term survival rates, and improve quality of life. The objective of this study is to present an automated analysis and classification system for breast cancer using clinical markers such as tumor shape, orientation, margin, and surrounding tissue. The novelty and uniqueness of the study lie in the approach of considering medical features based on the diagnosis of radiologists. Using clinical markers, a graph is generated where each feature is represented by a node, and the connection between them is represented by an edge which is derived through Pearson's correlation method. A graph convolutional network (GCN) model is proposed to classify breast tumors into benign and malignant, using the graph data. Several statistical tests are performed to assess the importance of the proposed features. The performance of the proposed GCN model is improved by experimenting with different layer configurations and hyper-parameter settings. Results show that the proposed model has a 98.73% test accuracy. The performance of the model is compared with a graph attention network, a one-dimensional convolutional neural network, and five transfer learning models, ten machine learning models, and three ensemble learning models. The performance of the model was further assessed with three supplementary breast cancer ultrasound image datasets, where the accuracies are 91.03%, 94.37%, and 89.62% for Dataset A, Dataset B, and Dataset C (combining Dataset A and Dataset B) respectively. Overfitting issues are assessed through k-fold cross-validation. Several variants are utilized to present a more rigorous and fair evaluation of our work, especially the importance of extracting clinically relevant features. Moreover, a GCN model using graph data can be a promising solution for an automated feature-based breast image classification system.

FARN: Fetal Anatomy Reasoning Network for Detection with Global Context Semantic and Local Topology Relationship.

Accurate recognition of fetal anatomical structure is a pivotal task in ultrasound (US) image analysis. Sonographers naturally apply anatomical knowledge and clinical expertise to recognizing key anatomical structures in complex US images. However, mainstream object detection approaches usually treat each structure recognition separately, overlooking anatomical correlations between different structures in fetal US planes. In this work, we propose a Fetal Anatomy Reasoning Network (FARN) that incorporates two kinds of relationship forms: a global context semantic block summarized with visual similarity and a local topology relationship block depicting structural pair constraints. Specifically, by designing the Adaptive Relation Graph Reasoning (ARGR) module, anatomical structures are treated as nodes, with two kinds of relationships between nodes modeled as edges. The flexibility of the model is enhanced by constructing the adaptive relationship graph in a data-driven way, enabling adaptation to various data samples without the need for predefined additional constraints. The feature representation is further enhanced by aggregating the outputs of the ARGR module. Comprehensive experimental results demonstrate that FARN achieves promising performance in detecting 37 anatomical structures across key US planes in tertiary obstetric screening. FARN effectively utilizes key relationships to improve detection performance, demonstrates robustness to small-scale, similar, and indistinct structures, and avoids some detection errors that deviate from anatomical norms. Overall, our study serves as a resource for developing efficient and concise approaches to model inter-anatomy relationships.

Multi-Derivational Parsing of Vague Languages - the New Paradigm of Syntactic Pattern Recognition.

The new paradigm of syntactic pattern recognition, SPR, which uses multi-derivational parsing of vague languages is introduced in the paper. The methodology proposed addresses the issue of the recognition of vague/distorted patterns which is one of the important open problems in the area. The concept of the vague language of patterns and the efficient parsing method based on the class of dynamically programmed grammars are introduced. A vague language is defined with vague primitives which are vectors of "neighboring" primitives associated with measures of distance, probability, fuzziness, etc. The use of vague primitives allows us to identify b best structural templates during multi-derivational parsing that can be used for getting more adequate final result. The generic architecture of SPR system based on the approach proposed together with the system's applications for short-term electrical load forecasting and for analysis of ultrasound images in order to diagnose congenital defects of fetal palates are presented. The results of the experimental studies are discussed.

Reliability of ultrasound measurements of muscle thickness and echointensity in older adults with type 2 diabetes

Ultrasound is an emerging tool for muscle-based measurements. However, the analysis of hyperechoic images (high muscle fat infiltration, as in type 2 diabetes [T2D]) can be challenging. ObjectiveTo evaluate the reliability of ultrasound image analysis of muscle thickness and echointensity in older adults with T2D and non-diabetic controls by experienced and novice analysts. We hypothesized that reliability would be high for the experienced analysts, and lower, but acceptable, for a novice analyst. MethodsWe recruited three groups: 1) adults >60 years with T2D; 2) age- and sex-matched normoglycemic controls; and 3) healthy adults 18–35 years. All participants underwent ultrasound imaging of the abdomen and anterior thigh. Ultrasound images were de-identified, randomized, and separately analyzed by two expert analysts (>3 years experience each). A subset of images was also analyzed by a novice analyst with no prior experience. Inter- and intra-rater reliability was assessed using coefficients of variation (CVs), intraclass correlation coefficients (ICCs), and Bland-Altman analysis. ResultsFor the expert analysts, ICCs were >0.90 and CVs were <10 % for all measurements, regardless of participant group. For the novice analyst, all ICCs were >0.90, except for rectus abdominus thickness measurements in the T2D group (ICC: 0.659 [95%CI: -0.134, 0.911]). CVs were <10 % for all measurements, except for rectus abdominus thickness in the T2D group (CV: 12.7 %). Ultrasound image analysis of muscle thickness and echointensity by experienced analysts was highly reliable. ConclusionNovice and expert analysts produced comparable measurements, except for rectus abdominus thickness in images obtained from older adults with T2D.

Automated deep bottleneck residual 82-layered architecture with Bayesian optimization for the classification of brain and common maternal fetal ultrasound planes.

Despite a worldwide decline in maternal mortality over the past two decades, a significant gap persists between low- and high-income countries, with 94% of maternal mortality concentrated in low and middle-income nations. Ultrasound serves as a prevalent diagnostic tool in prenatal care for monitoring fetal growth and development. Nevertheless, acquiring standard fetal ultrasound planes with accurate anatomical structures proves challenging and time-intensive, even for skilled sonographers. Therefore, for determining common maternal fetuses from ultrasound images, an automated computer-aided diagnostic (CAD) system is required. A new residual bottleneck mechanism-based deep learning architecture has been proposed that includes 82 layers deep. The proposed architecture has added three residual blocks, each including two highway paths and one skip connection. In addition, a convolutional layer has been added of size 3 × 3 before each residual block. In the training process, several hyper parameters have been initialized using Bayesian optimization (BO) rather than manual initialization. Deep features are extracted from the average pooling layer and performed the classification. In the classification process, an increase occurred in the computational time; therefore, we proposed an improved search-based moth flame optimization algorithm for optimal feature selection. The data is then classified using neural network classifiers based on the selected features. The experimental phase involved the analysis of ultrasound images, specifically focusing on fetal brain and common maternal fetal images. The proposed method achieved 78.5% and 79.4% accuracy for brain fetal planes and common maternal fetal planes. Comparison with several pre-trained neural nets and state-of-the-art (SOTA) optimization algorithms shows improved accuracy.

Adenomyosis in women undergoing hysterectomy for abnormal uterine bleeding associated with uterine leiomyomas

Background Uterine leiomyomas and adenomyosis are both common and often associated with abnormal uterine bleeding (AUB), including the symptom of heavy menstrual bleeding (HMB). Understanding the prevalence of adenomyosis in women with uterine leiomyomas could inform clinicians and patients in a way that may improve therapeutic approaches. Objective To explore the prevalence of adenomyosis in a group of women who underwent hysterectomy for AUB-L, to determine the prevalence of submucous leiomyomas, and to examine the utility of preoperative ultrasound to detect the presence of adenomyosis. Methods The Kaiser Permanente Hysterectomy Database (KPHD) was searched for women aged 18–52 undergoing hysterectomy for leiomyoma-associated chronic AUB (AUB-L) in 2018 and 2019. A target sample of 400 comprised those with at least 3 years in the Health System. Radiologists evaluated preoperative pelvic ultrasound images to determine leiomyoma size and level 2 FIGO type (submucous or other), and the linked electronic medical record abstracted for clinical features, including histopathological evidence of adenomyosis. Results Of the 370 subjects that met the study criteria, adenomyosis was identified via histopathology in 170 (45.9%). There was no difference in the adenomyosis prevalence with (47.1%) and without (43.0%) at least one submucous leiomyoma. Subgroup analysis of ultrasound images by an expert radiologist for the presence of adenomyosis demonstrated a positive predictive value of 54.0% and a negative predictive value of 43.4%. Conclusions Adenomyosis was present in almost half of this AUB-L cohort undergoing hysterectomy and was equally prevalent in those with and without submucous leiomyomas as determined by sonographic evaluation. The imaging findings are in accord with prior investigators and demonstrate that 2-D ultrasound is insensitive to the presence of adenomyosis when the uterus is affected by leiomyomas. Further research is necessary to determine the impact of various adenomyosis phenotypes on the presence and severity of the symptom of HMB.

An image registration-based self-supervised Su-Net for carotid plaque ultrasound image segmentation

Background and ObjectivesTotal Plaque Area (TPA) measurement is critical for early diagnosis and intervention of carotid atherosclerosis in individuals with high risk for stroke. The delineation of the carotid plaques is necessary for TPA measurement, and deep learning methods can automatically segment the plaque and measure TPA from carotid ultrasound images. A large number of labeled images is essential for training a good deep learning model, but it is very difficult to collect such large labeled datasets for carotid image segmentation in clinical practice. Self-supervised learning can provide a possible solution to improve the deep-learning models on small labeled training datasets by designing a pretext task to pre-train the models without using the segmentation masks. However, the existing self-supervised learning methods do not consider the feature presentations of object contours. MethodsIn this paper, we propose an image registration-based self-supervised learning method and a stacked U-Net (SSL-SU-Net) for carotid plaque ultrasound image segmentation, which can better exploit the semantic features of carotid plaque contours in self-supervised task training. ResultsOur network was trained on different numbers of labeled images (n = 10, 33, 50 and 100 subjects) and tested on 44 subjects from the SPARC dataset (n = 144, London, Canada). The network trained on the entire SPARC dataset was then directly applied to an independent dataset collected in Zhongnan hospital (n = 497, Wuhan, China). For the 44 subjects tested on the SPARC dataset, our method yielded a DSC of 80.25-89.18% and the produced TPA measurements, which were strongly correlated with manual segmentation (r = 0.965-0.995, ρ< 0.0001). For the Zhongnan dataset, the DSC was 90.3% and algorithm TPAs were strongly correlated with manual TPAs (r = 0.985, ρ< 0.0001). ConclusionsThe results demonstrate that our proposed method yielded excellent performance and good generalization ability when trained on a small labeled dataset, facilitating the use of deep learning in carotid ultrasound image analysis and clinical practice. The code of our algorithm is available https://github.com/a610lab/Registration-SSL.

Assessment of short-term effect of platelet-rich plasma treatment of tendinosis using texture analysis of ultrasound images.

Computer-aided diagnosis (i.e., texture analyses) tools are becoming increasingly beneficial methods to monitor subtle tissue changes. The aim of this pilot study was to investigate short-term effect of platelet rich plasma (PRP) treatment in supraspinatus and common extensor of the forearm tendinosis by using texture analysis of ultrasound (US) images as well as by clinical questionnaires. Thirteen patients (7 male and 6 female, age 36-60 years, mean age 51.2 ± 5.2) were followed after US guided PRP treatment for tendinosis of two tendons (9 patients with lateral epicondylitis and 4 with supraspinatus tendinosis). Clinical and US assessment was performed prior to as well as 3 months after PRP treatment with validated clinical questionnaires. Tissue response in tendons was assessed by using gray level run length matrix method (GLRLM) of US images. All patients improved of tendinosis symptoms after PRP treatment according to clinical questionnaires. Almost all GLRLM features were statistically improved 3 months after PRP treatment. GLRLM-long run high gray level emphasis (LRLGLE) revealed the best moderate positive and statistically significant correlation after PRP (r = 0.4373, p = 0.0255), followed by GLRLM-low gray level run emphasis (LGLRE) (r = 0.3877, p = 0.05). Texture analysis of tendinosis US images was a useful quantitative method for the assessment of tendon remodeling after minimally invasive PRP treatment. GLRLM features have the potential to become useful imaging biomarkers to monitor spatial and time limited tissue response after PRP, however larger studies with similar protocols are needed.

Style transfer-enabled Sim2Real framework for efficient learning of robotic transesophageal echocardiography using simulation

Recently, various machine learning techniques have been proposed to realize automatic ultrasound (US) image analysis for robotic US acquisition tasks. However, obtaining large amounts of real US images for training is usually expensive or even infeasible in some clinical applications, such as transesophageal echocardiography (TEE). An alternative is to build a simulator to generate synthetic US data for training, but the differences between simulated and real US images may result in poor model performance. This work presents a novel Sim2Real framework to learn robotic US image analysis based on simulated data to address the challenges of limited real US data for robotic TEE. A style transfer model is proposed based on unsupervised contrastive learning to convert real US images into the simulation style. Moreover, a task-relevant model is designed to combine CNNs with vision transformers and trained only on the simulated data to generate task-dependent predictions. We demonstrate the effectiveness of our method in an image regression task to predict the probe position based on US images in robotic TEE. Our results show that using only simulated US data and a small amount of unlabelled real data for training, our method can achieve comparable performance to semi-supervised and fully supervised learning methods. Moreover, the effectiveness of our previously proposed CT-based US image simulation method is also indirectly confirmed.

Accuracy of Prostate Volume Measurement on 2d Transabdominal USG Modality Using the Gradient Vector Flow (GVF) Segmentation Application Measurement Technique

In the field of radiology, supporting examinations to evaluate the prostate include examination with X-rays, Magnetic Resonance Imaging (MRI), and ultrasonography (USG). Of all imaging examinations that have been carried out throughout the world, it is estimated that around 25% use ultrasound. The general objective of this research is to analyze the design of the Gradient Vector Flow (GVF) segmentation algorithm on 2D ultrasound images to measure prostate volume in the adult age group. This study created a Gradient Vector Flow (GVF) segmentation application for the prostate organ to calculate the prostate volume calculation. RnD research with an experimental approach was carried out by creating a Gradient Vector Flow (GVF) segmentation design application, tested on 15 DICOM-based ultrasound images, measuring and calculating prostate volume using caliper measurement on ultrasound equipment and Gradient Vector Flow segmentation application. Measurements were made by an expert (sonographer) as an observer with data analysis using SPSS statistics. Analysis of ultrasound image of prostate volume using caliper measurement technique and Gradient Vector Flow (GVF) segmentation is similar to a p-value of 0.950 (&gt; 0.05). Measurement with caliper measurements has similarities shown by the mean rank value of Gradient Vector Flow (GVF) of 15.60 with a mean rank value of caliper measurement of 15.40. GVF application measurement on transabdominal ultrasound prostate volume measurement is similar to caliper measurement and can be used to get more accurate results on objects with less clear edge characteristics. However, the caliper measurement method can be used more efficiently.

GMRLNet: A graph-based manifold regularization learning framework for placental insufficiency diagnosis on incomplete multimodal ultrasound data.

Multimodal analysis of placental ultrasound (US) and microflow imaging (MFI) could greatly aid in the early diagnosis and interventional treatment of placental insufficiency (PI), ensuring a normal pregnancy. Existing multimodal analysis methods have weaknesses in multimodal feature representation and modal knowledge definitions and fail on incomplete datasets with unpaired multimodal samples. To address these challenges and efficiently leverage the incomplete multimodal dataset for accurate PI diagnosis, we propose a novel graph-based manifold regularization learning (MRL) framework named GMRLNet. It takes US and MFI images as input and exploits their modality-shared and modality-specific information for optimal multimodal feature representation. Specifically, a graph convolutional-based shared and specific transfer network (GSSTN) is designed to explore intra-modal feature associations, thus decoupling each modal input into interpretable shared and specific spaces. For unimodal knowledge definitions, graph-based manifold knowledge is introduced to describe the sample-level feature representation, local inter-sample relations, and global data distribution of each modality. Then, an MRL paradigm is designed for inter-modal manifold knowledge transfer to obtain effective cross-modal feature representations. Furthermore, MRL transfers the knowledge between both paired and unpaired data for robust learning on incomplete datasets. Experiments were conducted on two clinical datasets to validate the PI classification performance and generalization of GMRLNet. State-of-the-art comparisons show the higher accuracy of GMRLNet on incomplete datasets. Our method achieves 0.913 AUC and 0.904 balanced accuracy (bACC) for paired US and MFI images, as well as 0.906 AUC and 0.888 bACC for unimodal US images, illustrating its application potential in PI CAD systems.