Shape-based kidney detection and segmentation in three-dimensional abdominal ultrasound images.

We have introduced a three-dimensional (3-D) ultrasound imaging system for use in a combined ultrasound and near-infrared (NIR) imager for breast imaging. Compared with commercial ultrasound scanners which can only provide a single 2-D cross-section image at one scan, this 3-D system can provide 3-D co-registered ultrasound images for constraining regions of interest for 3-D NIR imaging reconstruction at one scan. The 3-D high-resolution ultrasound system consists of a state-of-the-art 1.75D ultrasound array of 1280 elements manufactured by Tetrad Inc. We have developed corresponding circuitry to drive the 1.75D array. The circuitry consists of 1280 parallel transmission channels, a 1280-to-40 multiplexer and 40 parallel receiving/data acquisition channels. A Texas Instrument DSP board has been embedded to speed up on-board data processing. We have tested the 3-D ultrasound imaging system with a 3-D ultrasound calibration phantom.

The endometrial–myometrial junction: a fresh look at a busy crossing

Cross-modal Transfer Learning Based on an Improved CycleGAN Model for Accurate Kidney Segmentation in Ultrasound Images

Ultrasound (US) image segmentation is a difficult task because of its heavy speckle noise, low quality and blurry boundaries. In this paper, a new neural network based method is proposed for ultrasound images segmentation. A modified self organizing map (SOM) network, named finite impulse response SOM (FIR-SOM), is utilized to segment ultrasound images. A two dimensional (2D) discrete wavelet transform (DWT) is used to build the input feature space of the network. Experimental results show that FIR-SOM discovers the pattern of the input image properly and is robust against noise. Segmentation results of breast ultrasound images (BUS) demonstrate that there is a strong correlation between tumor region selected by a physician and the tumor region segmented by our proposed method.

The technique of three-dimensional ultrasound imaging has been developed over the past 15 years, and has been particularly embraced by the field of obstetrics. More recently, advances have been made in gynecological applications, with promise demonstrated in pelvic floor and lower urinary tract imaging. A clinically useful extension of three-dimensional ultrasound imaging of the lower urinary tract is the assessment of urethral bulking agent therapy, as three-dimensional ultrasound imaging provides an objective means of assessing technical outcomes (i.e. periurethral position, configuration and volume of bulking agent). Our aim is to review recent developments in the use of three-dimensional ultrasound imaging in female incontinence, focusing on its role in the assessment of women undergoing urethral bulking agent therapy for stress urinary incontinence. Three-dimensional ultrasound imaging provides reproducible, affordable and clinically relevant information in the assessment of urethral bulking agent therapy with collagen. Serial, long-term three-dimensional ultrasound imaging assessment has enabled a definition of the technical outcome associated with a durable improvement in continence. The optimal volume varies widely between patients, but a circumferential or horseshoe-shaped periurethral configuration of collagen is strongly correlated with a durable improvement in continence. This finding is in agreement with limited data from a two-dimensional ultrasound assessment of Macroplastique. Three-dimensional ultrasound imaging provides an objective means of assessing technical outcomes from urethral bulking agent therapy not available previously. The volume and configuration information obtained allows for rational therapeutic decision-making, particularly with regard to determining the need for re-injection and recognizing the failure of an adequate therapeutic trial.

In this paper, a novel level set approach is proposed for segmentation of medical ultrasound images. Considering the speckle noise and low contrast of medical ultrasound images, we add an extra stopping term into the formulation of level set evolution without re-initialization (LSEWR). Compared with traditional active contour models, the proposed level set approach has more flexible initialization and larger capture range. It is insensitive to the initial contour and larger time step can be used. The initial contour can be easily initialized as a circle or rectangular, thus achieving semi-automatic segmentation of ultrasound medical images. The experimental results show that the proposed method can be used for semi-automatic and high-quality segmentation of medical ultrasound images.

Vein grafts are placed to bypass diseased arteries in the lower limb when symptoms such as pain during walking, rest pain, and tissue necrosis occur. Frequent surveillance of these grafts is recommended since early detection and revision of stenoses improves the likelihood of long-term patency. We are using three-dimensional (3D) ultrasound imaging to measure progressive changes in vein graft geometry. Cross-sectional area measurements in planes normal to the vessel center axis are calculated from 3D surface reconstructions. Data sets from serial studies are registered in a common coordinate system using anatomical reference points and cross-sectional area measurements are compared at matched sites. Extended studies have tracked patients requiring surgical revision to enlarge the vessel lumen in stenotic regions of their grafts. Follow-up periods on 5 cases range from 30 to 79 weeks to date. Each revised graft segment demonstrated a decrease in cross-sectional area over time, ranging from 25% to 77%, averaged over the length of the revision. The sequential area measurements, combined with 3D surface displays, provide a record of remodeling patterns and rates at specific sites within the grafts.

A new two-stream network based on feature separation and complementation for ultrasound image segmentation

Multiparametric ultrasound (mpUS) is being investigated as an alternative to magnetic resonance imaging (MRI) for detection of prostate cancer (PC). Automated prostate segmentation facilitates workflows, and zonal segmentation can aid in PC diagnosis, accounting for differences in imaging characteristics and tumor incidence. Our aim was to develop a deep learning algorithm that can automatically segment the prostate and its zones on three-dimensional (3D) contrast-enhanced ultrasound (CEUS) and conventional brightness-mode (B-mode) images (NCT04605276). A total of 259 3D mpUS images were collected from men with suspicion for PC in a prospective multicenter trial to develop a computer-aided diagnosis system for PC. Manual segmentation was performed using a custom tool, and an algorithm was developed using a convolutional neural network based on the U-Net architecture. Cross-validation of the automated segmentation algorithm revealed Dice similarity coefficients (DSCs) of 0.91 (95% confidence interval [CI] 0.90-0.91) for CEUS and 0.94 (95% CI 0.93-0.94) for B-mode ultrasound for 3D prostate segmentation. Zonal segmentation was less accurate, with DSCs of 0.83 (95% CI 0.82-0.84) for CEUS and 0.86 (95% CI 0.85-0.87) for B-mode ultrasound. There was high agreement for prostate volume between automatic segmentation on CEUS and physician-estimated volumes on MRI (R2=0.96). Qualitative assessment of prostate segmentation using a scale from 1 to 5 revealed a median grade of 5 (interquartile range [IQR] 4-5) for manual segmentation and 4 (IQR 4-5) for automated segmentation (p=0.10). Our deep learning algorithm demonstrated strong performance for automatic prostate and zonal segmentation from 3D CEUS and B-mode ultrasound images. We developed a computer tool to automatically identify the prostate in three-dimensional ultrasound images. The results show high accuracy and closely match manual assessments by urologists. This tool has potential for use in a computer-aided diagnostic system for prostate cancer.

Objective An improved method for obtaining pseudo-computed tomography (CTps) based on ultrasound deformation field. Methods The three-dimensional image data of computed tomography and ultrasound for three postoperative cervical cancer patients were selected, including the CT (CTsim) and ultrasound (USsim) images obtained during the simulated positioning stage, and the cone beam CT (CBCT) and ultrasound images obtained during the positioning verification stage of the treatment one week later. Binary masks of the OROI and OROW were created and applied in ultrasound image registration; thus, the deformation field was obtained. The deformation field was applied to CTsim images and different pseudo-CT images were obtained. Similarities between these pseudo-CT images and those of CBCT were compared, and registration accuracies between pseudo-CT images under different binary masks and CTsim were discussed. Results The averages of the correlation coefficient between pseudo-CT based on OROI, OROW, no binary mask and CBCT were 0.95, 0.82 and 0.64 respectively. The average of the normalized mean square Error were 0.12, 0.42 and 0.57 respectively. Conclusion The pseudo-CT based on OROI binary mask matches the best with CTsim and achieves the highest similarity with CBCT. Key words: Tomography, X-ray computed; Three-dimensional ultrasound image; Pseudo-CT imagingimage guided radiation therapy

Validation and reproducibility of a high-resolution three-dimensional facial imaging system

Automated lesion segmentation in ultrasound (US) images based on deep learning (DL) approaches plays a crucial role in disease diagnosis and treatment. However, the successful implementation of these approaches is conditioned by large-scale and diverse annotated datasets whose obtention is tedious and expertise demanding. Although methods like generative adversarial networks (GANs) can help address sample scarcity, they are often associated with complex training processes and high computational demands, which can limit their practicality and feasibility, especially in resource-constrained scenarios. Therefore, this study is aimed at exploring new solutions to address the challenge of limited annotated samples in automated lesion delineation in US images. Specifically, we propose five distinct mixed sample augmentation strategies and assess their effectiveness using four deep segmentation models for the delineation of two lesion types: breast and thyroid lesions. Extensive experimental analyses indicate that the effectiveness of these augmentation strategies is strongly influenced by both the lesion type and the model architecture. When appropriately selected, these strategies result in substantial performance improvements, with the Dice and Jaccard indices increasing by up to 37.95% and 36.32% for breast lesions and 14.59% and 13.01% for thyroid lesions, respectively. These improvements highlight the potential of the proposed strategies as a reliable solution to address data scarcity in automated lesion segmentation tasks. Furthermore, the study emphasizes the critical importance of carefully selecting data augmentation approaches, offering valuable insights into how their strategic application can significantly enhance the performance of DL models.

Chronic low back pain (cLBP) profoundly impacts quality of life, yet its underlying mechanisms remain poorly understood. Three-dimensional (3D) ultrasound imaging offers valuable insights into cLBP but poses challenges of manual annotation due to large data volume and poor image quality. To address these issues, a Cross-Scan Fusion Network (CSFN) is proposed for anatomical tissue layer segmentation in 3D ultrasound images. CSFN leverages a VoxelMorph-based registration model with Projected Hausdorff Loss (PHDL) to generate synthetic image-mask pairs, enabling both sample-efficient learning (CSFN-SEL) and semi-supervised learning (CSFN-SSL). Our results demonstrated that CSFN-SEL achieved superior segmentation performance compared to fully supervised nnU-Net (baseline) across varying labeled samples, with mean Dice coefficients of 74.62% ± 2.45% (5 samples) and 80.93% ± 1.87% (19 samples). CSFN-SSL further improved performance, achieving 79.33% ± 1.96% and 81.14% ± 1.43% for 5 and 19 samples, respectively, significantly outperforming baseline methods (p < 0.05). These findings highlight the effectiveness of CSFN in enhancing segmentation accuracy and robustness under limited labeled data conditions, offering a feasible solution for advancing cLBP imaging and analysis.

Two-dimensional ultrasound guidance has been used as an adjunct for neural blockade. With the development of newer ultrasound technology, three-dimensional ultrasound imaging is now available and may offer improved visualization of anatomic structures and relationships. We describe the successful blockade of the popliteal nerve with three-dimensional ultrasound guidance and image description.

Segmentation of lesions in ultrasound images is widely used for preliminary diagnosis. In this paper, we develop an automatic segmentation algorithm for multiple types of lesions in ultrasound images. The proposed method is able to detect and segment lesions automatically as well as generate accurate segmentation results for lesion regions. In the detection step, two saliency detection frameworks which adopt global image information are designed to capture the differences between normal and abnormal organs as well as these between lesions and the normal tissues around them. In the segmentation step, three types of local information, i.e., image intensity, improved local binary patterns (LBP) features, and an edge indicator, are embedded into a modified level set framework to carry out the segmentation task. The cyst and carcinoma regions in the ultrasound images of the human kidneys can be automatically detected and segmented by using the proposed method. The efficiency and accuracy of the method are validated by quantitative evaluations and comparative measurements with three well-recognized segmentation methods. Specifically, the average precision and dice coefficient of the proposed method in segmenting renal cysts are 95.33% and 90.16%, respectively, while those in segmenting renal carcinomas are 94.22% and 91.13%, respectively. The average precision and dice coefficient of the proposed method are higher than those of three compared segmentation methods. The proposed method can efficiently detect and segment the renal lesions in ultrasound images. In addition, since the proposed method utilizes the differences between normal and abnormal organs as well as these between lesions and the normal tissues around them, it can be possibly extended to deal with lesions in other organs of ultrasound images as well as lesions in medical images of other modalities.