Deep learning (DL) algorithms have rapidly become a robust tool for analyzing medical
images. They have been used extensively for medical image segmentation as the first and …

Abstract Purpose of Review Opportunistic screening is a combination of techniques to
identify subjects of high risk for osteoporotic fracture using routine clinical CT scans …

CT vertebral segmentation plays an essential role in various clinical applications, such as
computer‐assisted surgical interventions, assessment of spinal abnormalities, and vertebral …

Vertebral labelling and segmentation are two fundamental tasks in an automated spine
processing pipeline. Reliable and accurate processing of spine images is expected to …

Keywords: CT, Computer Aided Diagnosis (CAD), Computer Applications-General
(Informatics), Convolutional Neural Network (CNN), Diagnosis, Neural Networks …

Spine parsing (ie, multi-class segmentation of vertebrae and intervertebral discs (IVDs)) for
volumetric magnetic resonance (MR) image plays a significant role in various spinal disease …

Precise segmentation and anatomical identification of the vertebrae provides the basis for
automatic analysis of the spine, such as detection of vertebral compression fractures or other …

With the advent of deep learning algorithms, fully automated radiological image analysis is
within reach. In spine imaging, several atlas-and shape-based as well as deep learning …

Accurate vertebrae segmentation from medical images plays an important role in clinical
tasks of surgical planning, diagnosis, kyphosis, scoliosis, degenerative disc disease …

Precise vertebrae segmentation is essential for the image-related analysis of spine
pathologies such as vertebral compression fractures and other abnormalities, as well as for …