A Modified CycleGAN for Multi-Organ Ultrasound Image Enhancement via Unpaired Pre-Training

Handheld ultrasound devices are known for their portability and affordability,making them widely utilized in underdeveloped areas and community healthcare for rapid diagnosis and early screening.However,the image quality of handheld ultrasound devices is not always satisfactory due to the limited equipment size,which hinders accurate diagnoses by doctors.At the same time,paired ultrasound images are difficult to obtain from the clinic because imaging process is complicated.Therefore,we propose a modified cycle generative adversarial network(cycleGAN) for ultrasound image enhancement from multiple organs via unpaired pre-training.We introduce an ultrasound image pre-training method that does not require paired images,alleviating the requirement for large-scale paired datasets.We also propose an enhanced block with different structures in the pre-training and fine-tuning phases,which can help achieve the goals of different training phases.To improve the robustness of the model,we add Gaussian noise to the training images as data augmentation.Our approach is effective in obtaining the best quantitative evaluation results using a small number of parameters and less training costs to improve the quality of handheld ultrasound devices.

Automatic Fetal Segmentation Designed on Computer-Aided Detection with Ultrasound Images

In the present research,we describe a computer-aided detection(CAD)method aimed at automatic fetal head circumference(HC)measurement in 2D ultrasonography pictures during all trimesters of pregnancy.The HC might be utilized toward determining gestational age and tracking fetal development.This automated approach is particularly valuable in low-resource settings where access to trained sonographers is limited.The CAD system is divided into two steps:to begin,Haar-like characteristics were extracted from ultrasound pictures in order to train a classifier using random forests to find the fetal skull.We identified the HC using dynamic programming,an elliptical fit,and a Hough transform.The computer-aided detection(CAD)program was well-trained on 999 pictures(HC18 challenge data source),and then verified on 335 photos from all trimesters in an independent test set.A skilled sonographer and an expert in medicine personally marked the test set.We used the crown-rump length(CRL)measurement to calculate the reference gestational age(GA).In the first,second,and third trimesters,the median difference between the standard GA and the GA calculated by the skilled sonographer stayed at 0.7±2.7,0.0±4.5,and 2.0±12.0 days,respectively.The regular duration variance between the baseline GA and the health investigator’s GA remained 1.5±3.0,1.9±5.0,and 4.0±14 a couple of days.The mean variance between the standard GA and the CAD system’s GA remained between 0.5 and 5.0,with an additional variation of 2.9 to 12.5 days.The outcomes reveal that the computer-aided detection(CAD)program outperforms an expert sonographer.When paired with the classifications reported in the literature,the provided system achieves results that are comparable or even better.We have assessed and scheduled this computerized approach for HC evaluation,which includes information from all trimesters of gestation.

Using shapes correlation for active contour segmentation of uterine fibroid ultrasound images in computer-aided therapy

Segmenting the lesion regions from the ultrasound （US） images is an important step in the intra-operative planning of some computer-aided therapies. High-Intensity Focused Ultrasound （HIFU）, as a popular computer-aided therapy, has been widely used in the treatment of uterine fibroids. However, such segmentation in HIFU remains challenge for two reasons： （1） the blurry or missing boundaries of lesion regions in the HIFU images and （2） the deformation of uterine fibroids caused by the patient＇s breathing or an external force during the US imaging process, which can lead to complex shapes of lesion regions. These factors have prevented classical active contour-based segmentation methods from yielding desired results for uterine fibroids in US images. In this paper, a novel active contour-based segmentation method is proposed, which utilizes the correlation information of target shapes among a sequence of images as prior knowledge to aid the existing active contour method. This prior knowledge can be interpreted as a unsupervised clustering of shapes prior modeling. Meanwhile, it is also proved that the shapes correlation has the low-rank property in a linear space, and the theory of matrix recovery is used as an effective tool to impose the proposed prior on an existing active contour model. Finally, an accurate method is developed to solve the proposed model by using the Augmented Lagrange Multiplier （ALM）. Experimental results from both synthetic and clinical uterine fibroids US image sequences demonstrate that the proposed method can consistently improve the performance of active contour models and increase the robustness against missing or misleading boundaries, and can greatly improve the efficiency of HIFU therapy.

A Novel Approach to Breast Tumor Detection: Enhanced Speckle Reduction and Hybrid Classification in Ultrasound Imaging

Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effectivetreatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of breastcancer fromultrasound images. The primary challenge is accurately distinguishing between malignant and benigntumors, complicated by factors such as speckle noise, variable image quality, and the need for precise segmentationand classification. The main objective of the research paper is to develop an advanced methodology for breastultrasound image classification, focusing on speckle noise reduction, precise segmentation, feature extraction, andmachine learning-based classification. A unique approach is introduced that combines Enhanced Speckle ReducedAnisotropic Diffusion (SRAD) filters for speckle noise reduction, U-NET-based segmentation, Genetic Algorithm(GA)-based feature selection, and Random Forest and Bagging Tree classifiers, resulting in a novel and efficientmodel. To test and validate the hybrid model, rigorous experimentations were performed and results state thatthe proposed hybrid model achieved accuracy rate of 99.9%, outperforming other existing techniques, and alsosignificantly reducing computational time. This enhanced accuracy, along with improved sensitivity and specificity,makes the proposed hybrid model a valuable addition to CAD systems in breast cancer diagnosis, ultimatelyenhancing diagnostic accuracy in clinical applications.

Secure Transmission of Compressed Medical Image Sequences on Communication Networks Using Motion Vector Watermarking

Medical imaging plays a key role within modern hospital management systems for diagnostic purposes.Compression methodologies are extensively employed to mitigate storage demands and enhance transmission speed,all while upholding image quality.Moreover,an increasing number of hospitals are embracing cloud computing for patient data storage,necessitating meticulous scrutiny of server security and privacy protocols.Nevertheless,considering the widespread availability of multimedia tools,the preservation of digital data integrity surpasses the significance of compression alone.In response to this concern,we propose a secure storage and transmission solution for compressed medical image sequences,such as ultrasound images,utilizing a motion vector watermarking scheme.The watermark is generated employing an error-correcting code known as Bose-Chaudhuri-Hocquenghem(BCH)and is subsequently embedded into the compressed sequence via block-based motion vectors.In the process of watermark embedding,motion vectors are selected based on their magnitude and phase angle.When embedding watermarks,no specific spatial area,such as a region of interest(ROI),is used in the images.The embedding of watermark bits is dependent on motion vectors.Although reversible watermarking allows the restoration of the original image sequences,we use the irreversible watermarking method.The reason for this is that the use of reversible watermarks may impede the claims of ownership and legal rights.The restoration of original data or images may call into question ownership or other legal claims.The peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)serve as metrics for evaluating the watermarked image quality.Across all images,the PSNR value exceeds 46 dB,and the SSIM value exceeds 0.92.Experimental results substantiate the efficacy of the proposed technique in preserving data integrity.

Structured magnetic resonance imaging and endoanal ultrasound anal fistulas reporting template(SMART):An interdisciplinary Delphi consensus

BACKGROUND There is still considerable heterogeneity regarding which features of cryptoglandular anal fistula on magnetic resonance imaging(MRI)and endoanal ultrasound(EAUS)are relevant to surgical decision-making.As a con-sequence,the quality and completeness of the report are highly dependent on the training and experience of the examiners.AIM To develop a structured MRI and EAUS template(SMART)reporting the minimum dataset of information for the treatment of anal fistulas.METHODS This modified Delphi survey based on the RAND-UCLA appropriateness for consensus-building was conducted between May and August 2023.One hundred and fifty-one articles selected from a systematic review of the lite-rature formed the database to generate the evidence-based statements for the Delphi study.Fourteen questions were anonymously voted by an interdisciplinary multidisciplinary group for a maximum of three iterative rounds.The degree of agreement was scored on a numeric 0–10 scale.Group consensus was defined as a score≥8 for≥80%of the panelists.RESULTS Eleven scientific societies(3 radiological and 8 surgical)endorsed the study.After three rounds of voting,the experts(69 colorectal surgeons,23 radiologists,2 anatomists,and 1 gastroenterologist)achieved consensus for 12 of 14 statements(85.7%).Based on the results of the Delphi process,the six following features of anal fistulas were included in the SMART:Primary tract,secondary extension,internal opening,presence of collection,coexisting le-sions,and sphincters morphology.CONCLUSION A structured template,SMART,was developed to standardize imaging reporting of fistula-in-ano in a simple,systematic,time-efficient way,providing the minimum dataset of information and visual diagram useful to refer-ring physicians.

Magnetic resonance image-guided versus ultrasound-guided high-intensity focused ultrasound in the treatment of breast cancer

Image-guided high-intensity focused ultrasound (HIFU) has been used for more than ten years, primarily in the treatment of liver and prostate cancers. HIFU has the advantages of precise cancer ablation and excellent protection of healthy tissue. Breast cancer is a common cancer in women. HIFU therapy, in combination with other therapies, has the potential to improve both oncologic and cosmetic outcomes for breast cancer patients by providing a curative therapy that conserves mammary shape. Currently, HIFU therapy is not commonly used in breast cancer treatment, and efforts to promote the application of HIFU is expected. In this article, we compare different image-guided models for HIFU and reviewed the status, drawbacks, and potential of HIFU therapy for breast cancer.

Vault predicting after implantable collamer lens implantation using random forest network based on different features in ultrasound biomicroscopy images

AIM:To analyze ultrasound biomicroscopy(UBM)images using random forest network to find new features to make predictions about vault after implantable collamer lens(ICL)implantation.METHODS:A total of 450 UBM images were collected from the Lixiang Eye Hospital to provide the patient’s preoperative parameters as well as the vault of the ICL after implantation.The vault was set as the prediction target,and the input elements were mainly ciliary sulcus shape parameters,which included 6 angular parameters,2 area parameters,and 2 parameters,distance between ciliary sulci,and anterior chamber height.A random forest regression model was applied to predict the vault,with the number of base estimators(n_estimators)of 2000,the maximum tree depth(max_depth)of 17,the number of tree features(max_features)of Auto,and the random state(random_state)of 40.0.RESULTS:Among the parameters selected in this study,the distance between ciliary sulci had a greater importance proportion,reaching 52%before parameter optimization is performed,and other features had less influence,with an importance proportion of about 5%.The importance of the distance between the ciliary sulci increased to 53% after parameter optimization,and the importance of angle 3 and area 1 increased to 5% and 8%respectively,while the importance of the other parameters remained unchanged,and the distance between the ciliary sulci was considered the most important feature.Other features,although they accounted for a relatively small proportion,also had an impact on the vault prediction.After parameter optimization,the best prediction results were obtained,with a predicted mean value of 763.688μm and an actual mean value of 776.9304μm.The R²was 0.4456 and the root mean square error was 201.5166.CONCLUSION:A study based on UBM images using random forest network can be performed for prediction of the vault after ICL implantation and can provide some reference for ICL size selection.

Diagnostic Accuracy and Complication Rates of Fusion Images Created Using Real-Time Ultrasound with CT for Identification of Peripheral Lung Lesions in Patients Undergoing Biopsy

Background: Fusion image improves lesion detectability and can be an effective tool for percutaneous ultrasound (US)-guide procedure. We describe the clinical benefit of US-guided lung biopsy using fusion image. Purpose: To retrospectively compare the diagnostic accuracy and complication rates of US-guided lung biopsy with B-mode alone and those of a fusion image created using real-time US and computed tomography (CT). Materials and Methods: Between September, 2013 and September, 2016, 50 peripheral lung lesions in 50 patients (40 males, 10 females;median, 74 years old) were performed by US-guided percutaneous cutting needle biopsy using the B-mode alone or fusion image. Final diagnoses were based on surgical outcomes or clinical follow-up results for at least 12 months after biopsy. To assess prebiopsy characteristics, all lesions were divided into two groups: group 1 (identification on B-mode) and group 2 (identification on fusion image). Results: Of 50 peripheral lesions, 40 lesions (80%) were detected by means of B-mode alone (group 1), and 10 lesions (20%) were identified by fusion image (group 2). The diagnostic accuracy of group 1 was 90% (36/40 lesions), and the diagnostic accuracy of group 2 was 100% (10/10 lesions). Nodule type and the size of the lesions showed significant group wise differences (p Conclusion: Fusion images created using real-time US and CT may be useful for identification of the minimal size of potential target lung lesions and may be more suitable for improved yields with US-guided lung biopsy.

Design and Experiments of Ultrasound Image-Guided Multi-DOF Robot System for Brachytherapy

To implant radioactive seeds through a needle precisely and safely, a novel multi-DOF surgical robotic system is presented in this paper for percutaneous prostate intervention through the patient’s perineum under real-time ultrasound image guidance. The proposed robot, which is designed with 9-DOF, consists of a 3-DOF automatic location platform for position adjustment, 2-DOF for automatic ultrasonic probe adjustment mounted with electromagnetic trackers, and 4-DOF for manually adjusting the guided template. Meanwhile, a new registration method based on the quaternion algorithm and least square method is developed, and the needle insertion is performed under the real-time guidance of a navigation system. Furthermore, the robot system has undergone some preliminary experiments with a laser tracker to evaluate the repeatability and accuracy of the robot system. The location error of the puncture needle tip can be controlled under 0.7 mm in air for the whole robotic system. The acquired results endorse the precision of the robot system for prostate seed implantation brachytherapy. © 2017, Tianjin University and Springer-Verlag GmbH Germany.

The Application Value of Ultrasound Imaging in the Differential Diagnosis of Benign and Malignant Breast Nodules of BI-RADS 3 and Above

Objective:To explore the diagnostic value of ultrasound imaging for breast nodules of breast imaging-reporting and data system(BI-RADS)category 3 and above.Methods:From June 2021 to July 2022,163 patients with breast nodules of BI-RADS 3 or above were selected as the research subjects.After pathological diagnosis,24 cases were malignant breast nodules of BI-RADS 3 or above,while 139 cases were benign breast nodules of BI-RADS 3 or above.The diagnosis rate of malignant and benign breast nodules of BI-RADS 3 or above,including 95%CI,was observed and analyzed.Results:The malignant and benign detection rates of conventional ultrasound were 88.63%and 75.00%,respectively,and the malignant and benign detection rates of ultrasound imaging were 93.18%and 87.50%,respectively,with 95%CIs greater than 0.7.Conclusion:Ultrasound imaging can help improve the diagnostic accuracy of benign and malignant breast nodules of BI-RADS 3 and above and reduce the misdiagnosis rate.

Data augmentation of ultrasound imaging for non-invasive white blood cell in vitro peritoneal dialysis

The limited amount of data in the healthcare domain and the necessity of training samples for increased performance of deep learning models is a recurrent challenge,especially in medical imaging.Newborn Solutions aims to enhance its non-invasive white blood cell counting device,Neosonics,by creating synthetic in vitro ultrasound images to facilitate a more efficient image generation process.This study addresses the data scarcity issue by designing and evaluating a continuous scalar conditional Generative Adversarial Network(GAN)to augment in vitro peritoneal dialysis ultrasound images,increasing both the volume and variability of training samples.The developed GAN architecture incorporates novel design features:varying kernel sizes in the generator’s transposed convolutional layers and a latent intermediate space,projecting noise and condition values for enhanced image resolution and specificity.The experimental results show that the GAN successfully generated diverse images of high visual quality,closely resembling real ultrasound samples.While visual results were promising,the use of GAN-based data augmentation did not consistently improve the performance of an image regressor in distinguishing features specific to varied white blood cell concentrations.Ultimately,while this continuous scalar conditional GAN model made strides in generating realistic images,further work is needed to achieve consistent gains in regression tasks,aiming for robust model generalization.

Estimation of muscle pennation angle in ultrasound images using the beamlet transform

Ultrasound imaging has been widely used to investigate the architecture properties of skeletal muscle, including the measurement of the pennation angle. In this study, we propose a beamlet-based algorithm to detect the straight line- shaped patterns of aponeurosis, fascicle or bone, and then to quantify the pennation angle ix1 ultrasound images. The results demonstrate that the proposed algorithm can well detect the pennatoin angles in thirty ultrasound images with the correlation coefficient of 0.945, the standard root mean square error of 0.682°, and the relative root mean square error of 3.498%. The results suggest that this beamlet-based algorithm provides an alternative approach for the orientation estimation in muscu- loskeletal ultrasound images.

Despeckling of Ultrasound Images Using Modified Local Statistics Mean Variance Filter

This article presents an improved method of despeckling the ultrasound medical images.In this paper a modified local statistics mean variance filter method has been proposed.In the proposed method,more consideration is given to local statistics since local statistical features are more important rather than global features.Various parameters like mean square error,peak signal to noise ratio,quality index,and structural similarity index measure are calculated to analyze the quality of the despeckled image.

3D Elastic Registration of Ultrasound Images Based on Skeleton Feature

In order to eliminate displacement and elastic deformation between images of adjacent frames in course of 3D ultrasonic image reconstruction, elastic registration based on skeleton feature was adopt in this paper. A new automatically skeleton tracking extract algorithm is presented, which can extract connected skeleton to express figure feature. Feature points of connected skeleton are extracted automatically by accounting topical curvature extreme points several times. Initial registration is processed according to barycenter of skeleton. Whereafter, elastic registration based on radial basis function are processed according to feature points of skeleton. Result of example demonstrate that according to traditional rigid registration, elastic registration based on skeleton feature retain natural difference in shape for organr s different part, and eliminate slight elastic deformation between frames caused by image obtained process simultaneously. This algorithm has a high practical value for image registration in course of 3D ultrasound image reconstruction.

Medical ultrasound image segmentation by modified local histogram range image method

Fast and satisfied medical ultrasound segmentation is known to be difficult due to speckle noises and other artificial effects. Since speckle noise is formed from random signals which are emitted by an ultrasound system, we can’t encounter the same way as other image noises. Lack of information in ultrasound images is another problem. Thus, segmentation results may not be accurate enough by means of customary image segmentation methods. Those methods that can specify undesirable effects and segment them by eliminating artificial effects, should be chosen. It seems to be a complicated work with high computational load. The current study presents a different approach to ultrasound image segmentation that relies mainly on local evaluation, named as local histogram range image method which is modified by means of discrete wavelet transform. Thus, a significant decrease in computational load is then achieved. The results show that it is possible for tissues to be segmented correctly.

A diffusion method based on polar coordinate for ultrasound images denoising

The paper presents a novel anisotropic diffusion approach to the problem of ultrasound images denoising based on the polar-coordinate representation.Local gradients based on the polar coordinate are introduced and they are more suitable for ultrasound images than horizontal gradients and vertical gradients commonly used in anisotropic diffusion methods.Moreover,an adaptive adjustment scheme for the threshold parameter in conduction functions is presented according to the incident angle of the ultrasonic beam with respect to the organ surface.Furthermore,based on the structure matrix,an edge-adaptive diffusion model is introduced,which can selectively preserve the edge from the blurring or smooth noise.Experimental results of real ultrasound images show the validity of the presented approach,which takes the physical imaging mechanism of ultrasonic devices into account.

Medical ultrasound image segmentation using genetic active contour

Image segmentation is one of the earliest and most important stages of image processing and plays an important role in both qualitative and quantitative analysis of medical ultrasound images but ultrasound images have low level of contrast and are corrupted with strong speckle noise. Due to these effects, segmentation of ultrasound images is very challenging and traditional image segmentation methods may not be leads to satisfactory results. The active contour method has been one of the widely used techniques for image segmentation;however, due to low quality of ultrasound images, it has encountered difficulties. In this paper, we presented a segmental method combined genetic algorithm and active contour with an energy minimization procedure based on genetic algorithms. This method have been proposed to overcome some limits of classical active contours, as con-tour initialization and local minima (speckle noise), and have been successfully applied on medical ultrasound images. Experimental result on medical ultrasound image show that our presented method only can correctly segment the circular tissue’s on ultra-sound images.

Recovery of photoacoustic images based on accurate ultrasound positioning

Photoacoustic microscopy is an in vivo imaging technology based on the photoacoustic effect.It is widely used in various biomedical studies because it can provide high-resolution images while being label-free,safe,and harmless to biological tissue.Polygon-scanning is an effective scanning method in photoacoustic microscopy that can realize fast imaging of biological tissue with a large field of view.However,in polygon-scanning,fluctuations of the rotating motor speed and the geometric error of the rotating mirror cause image distortions,which seriously affect the photoacoustic-microscopy imaging quality.To improve the image quality of photoacoustic microscopy using polygon-scanning,an image correction method is proposed based on accurate ultrasound positioning.In this method,the photoacoustic and ultrasound imaging data of the sample are simultaneously obtained,and the angle information of each mirror used in the polygon-scanning is extracted from the ultrasonic data to correct the photoacoustic images.Experimental results show that the proposed method can significantly reduce image distortions in photoacoustic microscopy,with the image dislocation offset decreasing from 24.774 to 10.365μm.

The Use of Ultrasound Images in Manual Therapy and Additionally in Assessment of Shoulder Impingement Syndrome

The use of the ultrasound imaging (USI) in physiotherapy is becoming increasingly common but is highly operator dependent and there are safe and professional issues regarding its practical use. Currently there are no specific training guidelines relating to physiotherapists using USI. The use of ultrasound technology for medical applications began in the 1950s and has proven to be an effective, safe, non-invasive, and relatively inexpensive tool for assessing morphologic characteristics and structural integrity of visceral organs and soft tissues. The use of ultrasound to assess muscle morphology and guide rehabilitation decision-making in physical therapy practice can be traced back to the late 1960s and has been found to be reliable and valid for specific muscles during particular movements. Over the last decade there has been rapid development of this technique with increased use both by clinicians and researchers. This method is defined in literature with the denomination of Rehabilitative Ultrasound Imaging (RUSI). In this work we will see how RUSI could be of help in the evaluation of shoulder impingement syndrome (SIS).