Accurate segmentation of lesions in medical images is of great significance for clinical
diagnosis and evaluation. The low contrast between lesions and surrounding tissues …

The last few years have seen a significant adoption of automated segmentation techniques
by medical professionals for precise clinical decision-making. Automated segmentation …

Segmentation of knee cartilage from Ultrasound (US) images is essential for various clinical
tasks in diagnosis and treatment planning of Osteoarthritis. Moreover, the potential use of …

Objectives: We developed a 3D U-Net-based deep convolutional neural network for the
automatic segmentation of the vertebral cortex. The purpose of this study was to evaluate the …

Quantitative evaluation of bones and ligaments around knee joint from magnetic resonance
imaging (MRI) often requires the boundaries of selected structures to be manually traced …

Evaluating the severity of knee osteoarthritis (OA) accounts for significant plain film workload
and is a crucial component of knee radiograph interpretation, which informs surgical …

Deep learning-based methods play an increasingly important role in bioimage analysis.
User-friendly tools are crucial for increasing the adoption of deep learning models and …

We present a method for the automated segmentation of knee bones and cartilage from
magnetic resonance imaging (MRI) that combines a priori knowledge of anatomical shape …

Precise segmentation of knee tissues from magnetic resonance imaging (MRI) is critical in
quantitative imaging and diagnosis. Convolutional neural networks (CNNs), which are state …

Automatic segmentation of knee bone structures is an important task in orthopedics
diagnosis of knee disease based on MRI images. Inspired by doctors' diagnosis of knee in …