AIMC Topic: Imaging, Three-Dimensional

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AI-driven framework to map the brain metabolome in three dimensions.

Nature metabolism
High-resolution spatial imaging is transforming our understanding of foundational biology. Spatial metabolomics is an emerging field that enables the dissection of the complex metabolic landscape and heterogeneity from a thin tissue section. Currentl...

Robust resolution improvement of 3D UTE-MR angiogram of normal vasculatures using super-resolution convolutional neural network.

Scientific reports
Contrast-enhanced UTE-MRA provides detailed angiographic information but at the cost of prolonged scanning periods, which may impose moving artifacts and affect the promptness of diagnosis and treatment of time-sensitive diseases like stroke. This st...

Stages prediction of Alzheimer's disease with shallow 2D and 3D CNNs from intelligently selected neuroimaging data.

Scientific reports
Detection of Alzheimer's Disease (AD) is critical for successful diagnosis and treatment, involving the common practice of screening for Mild Cognitive Impairment (MCI). However, the progressive nature of AD makes it challenging to identify its causa...

Systematic review of artificial intelligence methods for detection and segmentation of unruptured intracranial aneurysms using medical imaging.

Medical & biological engineering & computing
Unruptured intracranial aneurysms are protuberances that appear in cerebral arteries, and their diagnostic evaluation can be a complex, time-consuming, and exhaustive task. In recent years, computer-aided systems have been developed to improve diagno...

Artificial intelligence-based incisive canal visualization for preventing and detecting post-implant injury, using cone beam computed tomography.

International journal of oral and maxillofacial surgery
The aim of this study was to clinically validate an artificial intelligence (AI)-based tool for automatic segmentation of the mandibular incisive canal (MIC) on cone beam computed tomography (CBCT), enabling prevention and detection of iatrogenic imp...

Classification of skeletal discrepancies by machine learning based on three-dimensional facial scans.

International journal of oral and maxillofacial surgery
The aim of this study was to use machine learning (ML) to classify sagittal and vertical skeletal discrepancies in three-dimensional (3D) facial scans, as well as to evaluate shape variability. 3D facial scans from 435 pre-orthodontic patients were s...

LW-CTrans: A lightweight hybrid network of CNN and Transformer for 3D medical image segmentation.

Medical image analysis
Recent models based on convolutional neural network (CNN) and Transformer have achieved the promising performance for 3D medical image segmentation. However, these methods cannot segment small targets well even when equipping large parameters. Theref...

Accelerated intracranial time-of-flight MR angiography with image-based deep learning image enhancement reduces scan times and improves image quality at 3-T and 1.5-T.

Neuroradiology
PURPOSE: Three-dimensional time-of-flight magnetic resonance angiography (TOF-MRA) is effective for cerebrovascular disease assessment, but clinical application is limited by long scan times and low spatial resolution. Recent advances in deep learnin...

Generative AI extracts ecological meaning from the complex three dimensional shapes of bird bills.

PLoS computational biology
Data on the three dimensional shape of organismal morphology is becoming increasingly available, and forms part of a new revolution in high-throughput phenomics that promises to help understand ecological and evolutionary processes that influence phe...

UC-NeRF: Uncertainty-Aware Conditional Neural Radiance Fields From Endoscopic Sparse Views.

IEEE transactions on medical imaging
Visualizing surgical scenes is crucial for revealing internal anatomical structures during minimally invasive procedures. Novel View Synthesis is a vital technique that offers geometry and appearance reconstruction, enhancing understanding, planning,...