AIMC Topic: Imaging, Three-Dimensional

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Automated 3D segmentation of human vagus nerve fascicles and epineurium from micro-computed tomography images using anatomy-aware neural networks.

Journal of neural engineering
Objective.Precise segmentation and quantification of nerve morphology from imaging data are critical for designing effective and selective peripheral nerve stimulation (PNS) therapies. However, prior studies on nerve morphology segmentation suffer fr...

Preoperative CT imaging and machine learning models for predicting ureteral access sheath placement success in non-stented patients with ureteral calculi: a retrospective cohort study.

World journal of urology
OBJECTIVE: This study aims to both develop and evaluate a predictive model for ureteral access sheath(UAS)placement success using preoperative CT-based 3D ureteral imaging and machine learning techniques. Specifically, it investigates the impact of u...

Rebuilding the pelvis: advances in robotic-assisted management of complex pelvic fractures.

Computer assisted surgery (Abingdon, England)
Complex pelvic fractures are infamously challenging to fix surgically because of their fine anatomy and proximity to vital neurovascular structures. Traditional open reduction and internal fixation (ORIF) improves stability but is complicated by exce...

Deep-learning analysis of 3D microarchitectural remodeling in hypertrophic cardiomyopathy.

Science (New York, N.Y.)
Hypertrophic cardiomyopathy (HCM), a genetic heart disease defined by unexplained cardiac wall thickening, is a leading cause of sudden death worldwide. However, the three-dimensional organization of cardiac tissue underlying left ventricular hypertr...

CTA-based deep-learning integrated model for identifying irregular shape and aneurysm size of unruptured intracranial aneurysms.

Journal of neurointerventional surgery
BACKGROUND: Artificial intelligence can help to identify irregular shapes and sizes, crucial for managing unruptured intracranial aneurysms (UIAs). However, existing artificial intelligence tools lack reliable classification of UIA shape irregularity...

Mapping dental biofilms: from plaque index through planimetry to volumetric analysis.

Clinical oral investigations
OBJECTIVES: Conventional plaque assessment methods, such as clinical indices and planimetry, rely on plaque-disclosing agents and may overemphasize thin biofilm areas due to plaque thickness variations. This study introduces a digital 3D method to qu...

JMM-TGT: Self-supervised 3D action recognition through joint motion masking and topology-guided transformer.

PloS one
In the field of 3D skeleton action recognition, research on self-supervised learning methods has primarily focused on spatio-temporal feature modeling. However, these methods rely heavily on modeling single motion features, which limits their ability...

Multi-view hybrid encoder U-Net for 3D renal vascular medical image segmentation.

Scientific reports
Understanding the size, shape, branching angles, and morphological features of blood vessels in human tissue remains challenging. To address this, we propose a multi-view hybrid encoder U-Net for segmenting renal artery vessels. The encoder in this m...

Exploring the Application of HoloLens Mixed Reality Combined with Eye Tracking and Visual Perception Technologies in Pediatric Orthopedic 3D Education.

Journal of medical systems
This narrative review evaluates the current status, potential value, key challenges, and future directions of Microsoft HoloLens 2 mixed reality (MR) technology, with a particular focus on its built-in eye tracking and visual perception functions, in...

Deep learning-based 3D automatic segmentation of impacted canines in CBCT scans.

BMC oral health
BACKGROUND: Impacted canines are one of the most frequently encountered dental anomalies in maxillofacial practice. Accurate localization of these teeth is crucial for treatment planning, and Cone Beam Computed Tomography (CBCT) offers detailed 3D im...