AIMC Topic: Imaging, Three-Dimensional

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Automated 3D segmentation of the hyoid bone in CBCT using nnU-Net v2: a retrospective study on model performance and potential clinical utility.

BMC medical imaging
OBJECTIVE: This study aimed to identify the hyoid bone (HB) using the nnU-Net based artificial intelligence (AI) model in cone beam computed tomography (CBCT) images and assess the model's success in automatic segmentation.

Super-resolution of 3D medical images by generative adversarial networks with long and short-term memory and attention.

Scientific reports
Since 3D medical imaging data is a string of sequential images, there is a strong correlation between consecutive images. Deep convolutional networks perform well in extracting spatial features, but are less capable for processing sequence data compa...

Automated classification of chondroid tumor using 3D U-Net and radiomics with deep features.

Scientific reports
Classifying chondroid tumors is an essential step for effective treatment planning. Recently, with the advances in computer-aided diagnosis and the increasing availability of medical imaging data, automated tumor classification using deep learning sh...

Accurate Tracking of Locomotory Kinematics in Mice Moving Freely in Three-Dimensional Environments.

eNeuro
Marker-based motion capture (MBMC) is a powerful tool for precise, high-speed, three-dimensional tracking of animal movements, enabling detailed study of behaviors ranging from subtle limb trajectories to broad spatial exploration. Despite its proven...

CellSeg3D, Self-supervised 3D cell segmentation for fluorescence microscopy.

eLife
Understanding the complex three-dimensional structure of cells is crucial across many disciplines in biology and especially in neuroscience. Here, we introduce a set of models including a 3D transformer (SwinUNetR) and a novel 3D self-supervised lear...

MVT-Net: A novel cervical tumour segmentation using multi-view feature transfer learning.

PloS one
Cervical cancer is one of the most aggressive malignant tumours of the reproductive system, posing a significant global threat to women's health. Accurately segmenting cervical tumours in MR images remains a challenging task due to the complex charac...

High-definition motion-resolved MRI using 3D radial kooshball acquisition and deep learning spatial-temporal 4D reconstruction.

Physics in medicine and biology
To develop motion-resolved volumetric MRI with 1.1 mm isotropic resolution and scan times <5 min using a combination of 3D radial kooshball acquisition and spatial-temporal deep learning 4D reconstruction for free-breathing high-definition (HD) lung ...

3D-MRI brain glioma intelligent segmentation based on improved 3D U-net network.

PloS one
PURPOSE: To enhance glioma segmentation, a 3D-MRI intelligent glioma segmentation method based on deep learning is introduced. This method offers significant guidance for medical diagnosis, grading, and treatment strategy selection.

Same-model and cross-model variability in knee cartilage thickness measurements using 3D MRI systems.

PloS one
PURPOSE: Magnetic Resonance Imaging (MRI) based three-dimensional analysis of knee cartilage has evolved to become fully automatic. However, when implementing these measurements across multiple clinical centers, scanner variability becomes a critical...

Contribution of Labrum and Cartilage to Joint Surface in Different Hip Deformities: An Automatic Deep Learning-Based 3-Dimensional Magnetic Resonance Imaging Analysis.

The American journal of sports medicine
BACKGROUND: Multiple 2-dimensional magnetic resonance imaging (MRI) studies have indicated that the size of the labrum adjusts in response to altered joint loading. In patients with hip dysplasia, it tends to increase as a compensatory mechanism for ...