AIMC Topic: Imaging, Three-Dimensional

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Impact of three-dimensional prostate models during robot-assisted radical prostatectomy on surgical margins and functional outcomes.

BJU international
BACKGROUND: Robot-assisted radical prostatectomy (RARP) is the standard surgical procedure for the treatment of prostate cancer. RARP requires a trade-off between performing a wider resection in order to reduce the risk of positive surgical margins (...

Automated instance segmentation and registration of spinal vertebrae from CT-Scans with an improved 3D U-net neural network and corner point registration.

Computers in biology and medicine
This paper presents a rapid and robust approach for 3D volumetric segmentation, labelling, and registration of human spinal vertebrae from CT scans using an optimised and improved 3D U-Net neural network architecture. The network is designed by incor...

Dental caries detection in children using intraoral scans and deep learning.

Journal of dentistry
OBJECTIVE: This study aimed to demonstrate the use of deep learning for automating caries detection using intraoral scan data from children and to evaluate diagnostic agreement between the models' predictions and dental practitioner assessments on 3D...

A deep learning model for accurate segmentation of the Drosophila melanogaster brain from Micro-CT imaging.

Developmental biology
The use of microcomputed tomography (Micro-CT) for imaging biological samples has burgeoned in the past decade, due to increased access to scanning platforms, ease of operation, and the advance of software platforms that enable accurate microstructur...

Deep Learning Differentiates Papilledema, NAION, and Healthy Eyes With Unsegmented 3D OCT Volumes.

American journal of ophthalmology
OBJECTIVE: Deep learning (DL) has been used to differentiate papilledema from healthy eyes and optic disc elevation on fundus photos. As we described optic nerve head (ONH) and peripapillary retina (PPR) optical coherence tomography (OCT) features th...

Symbolic and hybrid AI for brain tissue segmentation using spatial model checking.

Artificial intelligence in medicine
Segmentation of 3D medical images, and brain segmentation in particular, is an important topic in neuroimaging and in radiotherapy. Overcoming the current, time consuming, practise of manual delineation of brain tumours and providing an accurate, exp...

Denoising of high-resolution 3D UTE-MR angiogram data using lightweight and efficient convolutional neural networks.

Magnetic resonance imaging
High-resolution magnetic resonance angiography (∼ 50 μm MRA) data plays a critical role in the accurate diagnosis of various vascular disorders. However, it is very challenging to acquire, and it is susceptible to artifacts and noise which limits its...

Real-time and accurate stereo matching via tri-fusion volume for stereo vision.

Neural networks : the official journal of the International Neural Network Society
In the field of real-time stereo matching, a concise and informative cost volume is crucial for achieving high efficiency and accuracy. To this end, in this paper, we propose the Tri-Fusion Volume (TFV) to effectively fuse both texture details and si...

LETA: Tooth Alignment Prediction Based on Dual-branch Latent Encoding.

IEEE transactions on visualization and computer graphics
Accurately determining the clinical positions for each tooth is essential in orthodontics, while most existing solutions heavily rely on inefficient manual design. In this paper, we present the LETA, a dual-branch Latent Encoding based 3D Tooth Align...

Efficient Integration of Neural Representations for Dynamic Humans.

IEEE transactions on visualization and computer graphics
While numerous studies have explored NeRF-based novel view synthesis for dynamic humans, they often require training that exceeds several hours, limiting their practicality. Efforts to improve training efficiency have also encountered challenges beca...