Segmentation of measurable images from standard plane of Graf hip ultrasonograms based on Mask Region-Based Convolutional Neural Network.

Objectives: This study proposed a Mask Region-Based Convolutional Neural Network (R-CNN)-based automatic segmentation to accurately detect the measurable standard plane of Graf hip ultrasonography images via segmentation of the labrum, lower limb of ilium, and the iliac wing.

Patients And Methods: The study examined the hip ultrasonograms of 675 infants (205 males, 470 females; mean age: 7±2.8 weeks; range, 3 to 20 weeks) recorded between January 2011 and January 2018.

Mask Region-Based Convolutional Neural Network segmentation of the humerus and scapula from proton density-weighted axial shoulder magnetic resonance images.

Objectives: This study aimed to evaluate the effectiveness of Mask Region-Based Convolutional Neural Network (R-CNN) in humerus and scapula segmentation.

Patients And Methods: The study included 665 axial proton density (PD)-weighted magnetic resonance images of 665 consecutive shoulder instability patients (412 males, 253 females; mean age: 27±5.2 years; range, 18 to 42 years) between January 2011 and December 2014.

Relationship of Altmetric Attention Score, Twitter Performance, and Dimensions Badge Value With Traditional Metrics in Top-Cited Anterior Cruciate Ligament Research Studies.

Background: Alternative citation metrics-such as the Altmetric Attention Score (AAS), number of tweets (TN), and dimensions badge value (DBV)-are emerging as new options for assessing the value of scientific works.

Purpose: To analyze the AAS of highly cited articles on the anterior cruciate ligament (ACL) and to assess the relationship between alternative and traditional metrics such as journal rankings and article citation performance.

Study Design: Cross-sectional study.

Artificial intelligence based detection of age-related macular degeneration using optical coherence tomography with unique image preprocessing.

Purpose: The aim of the study is to improve the accuracy of age related macular degeneration (AMD) disease in its earlier phases with proposed Capsule Network (CapsNet) architecture trained on speckle noise reduced spectral domain optical coherence tomography (SD-OCT) images based on an optimized Bayesian non-local mean (OBNLM) filter augmentation techniques.

Methods: A total of 726 local SD-OCT images were collected and labelled as 159 drusen, 145 dry AMD, 156 wet AMD and 266 normal. Region of interest (ROI) was identified.

Deep Convolutional Neural Network-Based Automatic Classification of Neonatal Hip Ultrasound Images: A Novel Data Augmentation Approach with Speckle Noise Reduction.

Neonatal hip ultrasound imaging has been widely used for a few decades in the diagnosis of developmental dysplasia of the hip (DDH). Graf's method of hip ultrasonography is still the most reproducible because of its classification system; yet, the reproducibility is questionable as a result of the high dependency on the skills of the sonographer and the evaluator. A computer-aided diagnosis system may help evaluators increase their precision in the diagnosis of DDH using Graf's method.

Segmentation of Bone with Region Based Active Contour Model in PD Weighted MR Images of Shoulder.

Proton density (PD) weighted MR images present inhomogeneity problem, low signal to noise ratio (SNR) and cannot define bone borders clearly. Segmentation of PD weighted images is hampered with these properties of PD weighted images which even limit the visual inspection. The purpose of this study is to determine the effectiveness of segmentation of humeral head from axial PD MR images with active contour without edge (ACWE) model.