AIMC Topic: Imaging, Three-Dimensional

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Dense cellular segmentation for EM using 2D-3D neural network ensembles.

Scientific reports
Biologists who use electron microscopy (EM) images to build nanoscale 3D models of whole cells and their organelles have historically been limited to small numbers of cells and cellular features due to constraints in imaging and analysis. This has be...

Hypergraph Neural Network for Skeleton-Based Action Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Recently, skeleton-based human action recognition has attracted a lot of research attention in the field of computer vision. Graph convolutional networks (GCNs), which model the human body skeletons as spatial-temporal graphs, have shown excellent re...

A deep learning based framework for the registration of three dimensional multi-modal medical images of the head.

Scientific reports
Image registration is a fundamental task in image analysis in which the transform that moves the coordinate system of one image to another is calculated. Registration of multi-modal medical images has important implications for clinical diagnosis, tr...

Under-exploration of Three-Dimensional Images Leads to Search Errors for Small Salient Targets.

Current biology : CB
Advances in 3D imaging technology are transforming how radiologists search for cancer and how security officers scrutinize baggage for dangerous objects. These new 3D technologies often improve search over 2D images but vastly increase the image data...

Deep learning-Based 3D inpainting of brain MR images.

Scientific reports
The detailed anatomical information of the brain provided by 3D magnetic resonance imaging (MRI) enables various neuroscience research. However, due to the long scan time for 3D MR images, 2D images are mainly obtained in clinical environments. The p...

4D deep image prior: dynamic PET image denoising using an unsupervised four-dimensional branch convolutional neural network.

Physics in medicine and biology
Although convolutional neural networks (CNNs) demonstrate the superior performance in denoising positron emission tomography (PET) images, a supervised training of the CNN requires a pair of large, high-quality PET image datasets. As an unsupervised ...

Automatic Cephalometric Landmark Identification System Based on the Multi-Stage Convolutional Neural Networks with CBCT Combination Images.

Sensors (Basel, Switzerland)
This study was designed to develop and verify a fully automated cephalometry landmark identification system, based on multi-stage convolutional neural networks (CNNs) architecture, using a combination dataset. In this research, we trained and tested ...

Automatic segmentation of ventricular volume by 3D ultrasonography in post haemorrhagic ventricular dilatation among preterm infants.

Scientific reports
To train, evaluate, and validate the application of a deep learning framework in three-dimensional ultrasound (3D US) for the automatic segmentation of ventricular volume in preterm infants with post haemorrhagic ventricular dilatation (PHVD). We tra...

Ultrasound volume projection image quality selection by ranking from convolutional RankNet.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Periodic inspection and assessment are important for scoliosis patients. 3D ultrasound imaging has become an important means of scoliosis assessment as it is a real-time, cost-effective and radiation-free imaging technique. With the generation of a 3...

3D multi-scale deep convolutional neural networks for pulmonary nodule detection.

PloS one
With the rapid development of big data and artificial intelligence technology, computer-aided pulmonary nodule detection based on deep learning has achieved some successes. However, the sizes of pulmonary nodules vary greatly, and the pulmonary nodul...