RESUMO
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.
Assuntos
Técnicas de Imagem por Elasticidade , Músculos Paraespinais , Postura Sentada , Humanos , Decúbito Ventral/fisiologia , Masculino , Feminino , Músculos Paraespinais/fisiologia , Músculos Paraespinais/diagnóstico por imagem , Adulto , Adulto Jovem , Voluntários Saudáveis , Região Lombossacral/diagnóstico por imagem , Região Lombossacral/fisiologia , Vértebras Lombares/diagnóstico por imagem , Vértebras Lombares/fisiologia , Postura/fisiologiaRESUMO
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.