AIMC Topic: Spine

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Accelerated 3D qCEST of the Spine in a Porcine Model Using MR Multitasking at 3T.

NMR in biomedicine
To assess lower back pain using quantitative chemical exchange saturation transfer (qCEST) imaging in a porcine model by comparing exchange rate maps obtained from multitasking qCEST with conventional qCEST. Use a permuted random forest (PRF) model t...

Automated instance segmentation and registration of spinal vertebrae from CT-Scans with an improved 3D U-net neural network and corner point registration.

Computers in biology and medicine
This paper presents a rapid and robust approach for 3D volumetric segmentation, labelling, and registration of human spinal vertebrae from CT scans using an optimised and improved 3D U-Net neural network architecture. The network is designed by incor...

AI-driven optimization of spinal implant design using parametric modelling.

Colloids and surfaces. B, Biointerfaces
This study aimed to enhance vertebral implant design by using a parametric spine model and advanced simulation techniques to evaluate biomechanical behaviours under dynamic physiological conditions using Finite Element Analysis (FEA) in ANSYS Workben...

Integrating Artificial Intelligence into Mixed Reality for Back Detection and Virtual 3D Spine Visualization on Scoliosis Patients.

Studies in health technology and informatics
Adolescent Idiopathic Scoliosis is a complex three-dimensional spinal deformity that typically develops between the ages of 10 and 18 years. If untreated, this condition can significantly impair a patient's quality of life and functional capabilities...

LiDSCUNet++: A lightweight depth separable convolutional UNet++ for vertebral column segmentation and spondylosis detection.

Research in veterinary science
Accurate computer-aided diagnosis systems rely on precise segmentation of the vertebral column to assist physicians in diagnosing various disorders. However, segmenting spinal disks and bones becomes challenging in the presence of abnormalities and c...

Generation of synthetic CT-like imaging of the spine from biplanar radiographs: comparison of different deep learning architectures.

Neurosurgical focus
OBJECTIVE: This study compared two deep learning architectures-generative adversarial networks (GANs) and convolutional neural networks combined with implicit neural representations (CNN-INRs)-for generating synthetic CT (sCT) images of the spine fro...

Zero-shot segmentation of spinal vertebrae with metastatic lesions: an analysis of Meta's Segment Anything Model 2 and factors affecting learning free segmentation.

Neurosurgical focus
OBJECTIVE: Accurate vertebral segmentation is an important step in imaging analysis pipelines for diagnosis and subsequent treatment of spinal metastases. Segmenting these metastases is especially challenging given their radiological heterogeneity. C...

Next-generation surgical navigation: Marker-less multi-view 6DoF pose estimation of surgical instruments.

Medical image analysis
State-of-the-art research of traditional computer vision is increasingly leveraged in the surgical domain. A particular focus in computer-assisted surgery is to replace marker-based tracking systems for instrument localization with pure image-based 6...