AIMC Topic: X-Ray Microtomography

Clear Filters Showing 11 to 20 of 75 articles

Enhancing synchrotron radiation micro-CT images using deep learning: an application of Noise2Inverse on bone imaging.

Journal of synchrotron radiation
In bone-imaging research, in situ synchrotron radiation micro-computed tomography (SRµCT) mechanical tests are used to investigate the mechanical properties of bone in relation to its microstructure. Low-dose computed tomography (CT) is used to prese...

A modular cage may prevent endplate damage and improve spinal deformity correction.

Clinical biomechanics (Bristol, Avon)
BACKGROUND: Anterior lumbar interbody fusion is performed to fuse pathological spinal segments, generally, with a monobloc cage inserted by impact forces. Recently developed three-part modular cages attempt to reduce the impact forces, minimize the d...

The effect of cryopreservation on enamel microcracks - A μCT analysis using a deep learning algorithm.

Cryobiology
To date, the effect of cryopreservation on microcracks in the dental enamel remains unclear. These enamel microcracks are very thin, at the limit of visibility and their segmentation is beyond the capabilities of traditional image analysis. The objec...

A robust deep learning approach for segmenting cortical and trabecular bone from 3D high resolution µCT scans of mouse bone.

Scientific reports
Recent advancements in deep learning have significantly enhanced the segmentation of high-resolution microcomputed tomography (µCT) bone scans. In this paper, we present the dual-branch attention-based hybrid network (DBAHNet), a deep learning archit...

Segmentation of cortical bone, trabecular bone, and medullary pores from micro-CT images using 2D and 3D deep learning models.

Anatomical record (Hoboken, N.J. : 2007)
Computed tomography (CT) enables rapid imaging of large-scale studies of bone, but those datasets typically require manual segmentation, which is time-consuming and prone to error. Convolutional neural networks (CNNs) offer an automated solution, ach...

Cell quantification at the osteochondral interface from synchrotron radiation phase contrast micro-computed tomography images using a deep learning approach.

Scientific reports
Osteochondral interface consists of two tissues: the calcified cartilage (CC) containing chondrocytes, and subchondral bone (SCB) containing osteocytes that interact with each other. In this study, we propose a new method for the three-dimensional (3...

Reconstructing 3D histological structures using machine learning (artificial intelligence) algorithms.

Pathologie (Heidelberg, Germany)
BACKGROUND: Histomorphometry is currently the gold standard for bone microarchitectural examinations. This relies on two-dimensional (2D) sections to deduce the spatial properties of structures. Micromorphometric parameters are calculated from these ...

Self-adaptive deep learning-based segmentation for universal and functional clinical and preclinical CT image analysis.

Computers in biology and medicine
BACKGROUND: Methods to monitor cardiac functioning non-invasively can accelerate preclinical and clinical research into novel treatment options for heart failure. However, manual image analysis of cardiac substructures is resource-intensive and error...

Identification of Root Canal Morphology in Fused-rooted Mandibular Second Molars From X-ray Images Based on Deep Learning.

Journal of endodontics
INTRODUCTION: Understanding the intricate anatomical morphology of fused-rooted mandibular second molars (MSMs) is essential for root canal treatment. The present study utilized a deep learning approach to identify the three-dimensional root canal mo...

Characterization and quantification of in-vitro equine bone resorption in 3D using μCT and deep learning-aided feature segmentation.

Bone
High cyclic strains induce formation of microcracks in bone, triggering targeted bone remodeling, which entails osteoclastic resorption. Racehorse bone is an ideal model for studying the effects of high-intensity loading, as it is subject to focal fo...