Segmentation of anatomical components is a major step in creating accurate and realistic
3D models of the human body, which are used in many clinical applications, including …

Segmentation of anatomical components is a major step in creating accurate and realistic
3D models of the human body, which are used in many clinical applications, including …

Segmentation of anatomical components is a critical step in creating accurate and realistic
3D models of the human body, which are employed in a wide range of clinical applications …

Purpose To describe and evaluate a new segmentation method using deep convolutional
neural network (CNN), 3D fully connected conditional random field (CRF), and 3D simplex …

Minimally invasive surgery (MIS) is among the preferred procedures for treating a number of
ailments as patients benefit from fast recovery and reduced blood loss. The trade-off is that …

Automated segmentation of knee subchondral bone structures such as area and shape
using deep learning approaches is a significant task for medical MRI images. However …

This study aims at comparing the nnU-Net, an open-source deep learning framework, with a
previous customized U-Net model that we developed for the automatic segmentation of tibial …

Background and objective Segmentation is a crucial step in multiple biomechanics and
orthopedics applications. The time-intensiveness and expertise requirements of medical …

Knee disorders are common among the human population. Knee osteoarthritis (OA) is the
most widespread knee joint disorder, which may require surgical treatment. The detection …

Background and objectives Segmentation of regions of interest (ROIs) such as tumors and
bones plays an essential role in the analysis of musculoskeletal (MSK) images …