AIMC Topic: Imaging, Three-Dimensional

Clear Filters Showing 51 to 60 of 1894 articles

Novel Prototype and Exemplar (NPE) database: A set of 2700 novel 3D images with viewpoint and shape variations.

Behavior research methods
Many studies have used images of novel objects as experimental materials. Existing novel object databases do not provide diverse exemplars, and many studies need to manipulate or examine the diversity of exemplars. To fill this gap in experimental ma...

Template Learning: Deep learning with domain randomization for particle picking in cryo-electron tomography.

Nature communications
Cryo-electron tomography (cryo-ET) enables three-dimensional visualization of biomolecules and cellular components in their near-native state. A key challenge in cryo-ET data analysis is particle picking, often performed by template matching, which r...

A longitudinal dataset of tile and corresponding dermoscopic images with metadata for identifying skin cancers.

Scientific data
Machine learning classification algorithms have emerged as promising tools to support the early detection of skin cancers. Existing algorithms typically assess malignancy of skin lesions based on a single skin image. This is in contrast with how clin...

A deep learning algorithm for automatic 3D segmentation and quantification of hamstrings musculotendon injury from MRI.

Scientific reports
In high-velocity sports, hamstring strain injuries are common causes of missed play and have high rates of reinjury. Evaluating the severity and location of a hamstring strain injury, currently graded by a clinician using a semiqualitative muscle inj...

Three-dimensional ultrastructural characterization of Drosophila melanogaster hygrosensilla across humidity conditions.

PloS one
Understanding how organisms detect environmental humidity remains a fundamental problem in sensory biology. While specialised sensory neurons in insect antennae can detect changes in humidity, the mechanism underlying this ability is not fully unders...

An open deep learning-based framework and model for tooth instance segmentation in dental CBCT.

Clinical oral investigations
OBJECTIVES: Current dental CBCT segmentation tools often lack accuracy, accessibility, or comprehensive anatomical coverage. To address this, we constructed a densely annotated dental CBCT dataset and developed a deep learning model, OraSeg, for toot...

3D electroacoustic tomography image enhancement using deep learning with the SAM-Med3D encoder.

Physics in medicine and biology
To overcome the limitations of electroacoustic tomography (EAT) in clinical settings-particularly the artifacts and distortions caused by limited-angle data acquisition-and enable accurate, efficient visualization of electric field distributions for ...

A Deep Learning-Based Fully Automated Vertebra Segmentation and Labeling Workflow.

British journal of hospital medicine (London, England : 2005)
Spinal disorders, such as herniated discs and scoliosis, are highly prevalent conditions with rising incidence in the aging global population. Accurate analysis of spinal anatomical structures is a critical prerequisite for achieving high-precision ...

A 3D multi-task network for the automatic segmentation of CT images featuring hip osteoarthritis.

Biomedical physics & engineering express
Total hip arthroplasty (THA) is the primary treatment for end-stage hip osteoarthritis, with successful outcomes depending on precise preoperative planning that requires accurate segmentation and reconstruction of periarticular bone of the hip joint....

CQ-CNN: A lightweight hybrid classical-quantum convolutional neural network for Alzheimer's disease detection using 3D structural brain MRI.

PloS one
The automatic detection of Alzheimer's disease (AD) using 3D volumetric MRI data is a complex, multi-domain challenge that has traditionally been addressed by training classical convolutional neural networks (CNNs). With the rise of quantum computing...