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BACKGROUND: Multifidus is an important lumbar muscle with distinct superficial and deep fibers responsible for torque production and stabilization, respectively. Its mechanical properties change when transitioning from lying to sitting positions, necessitating enhanced stability. It holds crucial clinical relevance to assess these layers separately, especially in the sitting posture, which demands increased neuromuscular control compared to the prone position. OBJECTIVE: To compare lumbar multifidus stiffness in lying versus sitting postures, analyzing both superficial and deep layers. METHODS: Supersonic Shear Imaging captured elastographic images from 26 asymptomatic volunteers in prone and seated positions. RESULTS: Left multifidus shear modulus in lying: 5.98 ± 1.80/7.96 ± 1.59 kPa (deep/superficial) and sitting: 12.58 ± 4.22/16.04 ± 6.65 kPa. Right side lying: 6.08 ± 1.97/7.80 ± 1.76 kPa and sitting: 13.25 ± 4.61/17.95 ± 7.12 kPa. No side differences (lying p = 0.99, sitting p = 0.43). However, significant inter-postural differences occurred. CONCLUSION: Lumbar multifidus exhibits increased stiffness in sitting, both layers affected, with superior stiffness in superficial versus deep fibers. Applying these findings could enhance assessing multifidus stiffness changes, for classifying tension-induced low back pain stages. [ABSTRACT FROM AUTHOR]

Purpose: Computer‐aided diagnosis (CAD) systems on breast ultrasound (BUS) aim to increase the efficiency and effectiveness of breast screening, helping specialists to detect and classify breast lesions. CAD system development requires a set of annotated images, including lesion segmentation, biopsy results to specify benign and malignant cases, and BI‐RADS categories to indicate the likelihood of malignancy. Besides, standardized partitions of training, validation, and test sets promote reproducibility and fair comparisons between different approaches. Thus, we present a publicly available BUS dataset whose novelty is the substantial increment of cases with the above‐mentioned annotations and the inclusion of standardized partitions to objectively assess and compare CAD systems. Acquisition and Validation Methods: The BUS dataset comprises 1875 anonymized images from 1064 female patients acquired via four ultrasound scanners during systematic studies at the National Institute of Cancer (Rio de Janeiro, Brazil). The dataset includes biopsy‐proven tumors divided into 722 benign and 342 malignant cases. Besides, a senior ultrasonographer performed a BI‐RADS assessment in categories 2 to 5. Additionally, the ultrasonographer manually outlined the breast lesions to obtain ground truth segmentations. Furthermore, 5‐ and 10‐fold cross‐validation partitions are provided to standardize the training and test sets to evaluate and reproduce CAD systems. Finally, to validate the utility of the BUS dataset, an evaluation framework is implemented to assess the performance of deep neural networks for segmenting and classifying breast lesions. Data Format and Usage Notes: The BUS dataset is publicly available for academic and research purposes through an open‐access repository under the name BUS‐BRA: A Breast Ultrasound Dataset for Assessing CAD Systems. BUS images and reference segmentations are saved in Portable Network Graphic (PNG) format files, and the dataset information is stored in separate Comma‐Separated Value (CSV) files. Potential Applications: The BUS‐BRA dataset can be used to develop and assess artificial intelligence‐based lesion detection and segmentation methods, and the classification of BUS images into pathological classes and BI‐RADS categories. Other potential applications include developing image processing methods like despeckle filtering and contrast enhancement methods to improve image quality and feature engineering for image description. [ABSTRACT FROM AUTHOR]

Introduction: Various signal-processing techniques have been proposed to extract quantitative information about internal structures of tissues from the original radio frequency (RF) signals instead of an ultrasound image. The quantifiable parameter called the mean scatterer spacing (MSS) can be useful to detect changes in the quasi-periodic microstructure of tissues such as the liver or the spleen, using ultrasonic signals. Methods: We evaluate and compare the performance of three classic methods of spectral estimation to calculate the MSS without operator intervention: Tufts-Kumaresan, SAC (Spectral Autocorrelation) and MUSIC (MUltiple SIgnal Classification). Initially the evaluations were performed with 10,000 signals simulated from a model in which the variables of interest are controlled, and then, real signals from sponge phantoms were used. Results: For the simulated signals, the performance of all three methods decreased with increasing Ad or jitter levels. For the sponges, none of the methods accurately estimated the pore size. Conclusion: For the simulated signals, Tufts-Kumaresan had the lowest performance, whereas SAC and MUSIC had similar results. For sponges, only Tufts-Kumaresan was able to detect the increase in the size of the pores of the sponge, although most often, it estimated sizes larger than expected. [ABSTRACT FROM AUTHOR]

Introduction: The use of tools for computer-aided diagnosis (CAD) has been proposed for detection and classification of breast cancer. Concerning breast cancer image diagnosing with ultrasound, some results found in literature show that morphological features perform better than texture features for lesions differentiation, and indicate that a reduced set of features performs better than a larger one. Methods: This study evaluated the performance of support vector machines (SVM) with different kernels combinations, and neural networks with different stop criteria, for classifying breast cancer nodules. Twenty-two morphological features from the contour of 100 BUS images were used as input for classifiers and then a scalar feature selection technique with correlation was used to reduce the features dataset. Results: The best results obtained for accuracy and area under ROC curve were 96.98% and 0.980, respectively, both with neural networks using the whole set of features. Conclusion: The performance obtained with neural networks with the selected stop criterion was better than the ones obtained with SVM. Whilst using neural networks the results were better with all 22 features, SVM classifiers performed better with a reduced set of 6 features. [ABSTRACT FROM AUTHOR]

This work presents an ultrasonic method to characterize the layers of a stratified medium, using independent measurements of wave speed and thickness of each layer. The model, based on geometrical acoustics, includes refraction. Two transducers are used: one active (3.4 MHz) and a hydrophone as a receptor, which is moved laterally through 15 positions. The distance between the transducers and the delay between the echoes, from the interfaces separating the layers, received by them are used to estimate the speed and thickness. Three types of layered phantoms were used: Ph1 made with alcohol/acrylic, Ph2 made with polyvinyl chloride/water/acrylic, and Ph3 made with acrylic/water/polyvinyl chloride. The experimental results for speed of sound and layer thickness presented an experimental mean relative error, for thickness and wave speed, lower than 7.0% and 6.6%, respectively. [ABSTRACT FROM AUTHOR]