AIMC Topic: Imaging, Three-Dimensional

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Predicting real-time 3D deformation field maps (DFM) based on volumetric cine MRI (VC-MRI) and artificial neural networks for on-board 4D target tracking: a feasibility study.

Physics in medicine and biology
To predict real-time 3D deformation field maps (DFMs) using Volumetric Cine MRI (VC-MRI) and adaptive boosting and multi-layer perceptron neural network (ADMLP-NN) for 4D target tracking. One phase of a prior 4D-MRI is set as the prior phase, MRI. Pr...

Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem.

IEEE transactions on medical imaging
Volumetric imaging is an essential diagnostic tool for medical practitioners. The use of popular techniques such as convolutional neural networks (CNN) for analysis of volumetric images is constrained by the availability of detailed (with local annot...

Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection.

IEEE transactions on medical imaging
Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. Maximu...

Multiscale brain MRI super-resolution using deep 3D convolutional networks.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The purpose of super-resolution approaches is to overcome the hardware limitations and the clinical requirements of imaging procedures by reconstructing high-resolution images from low-resolution acquisitions using post-processing methods. Super-reso...

Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI With Limited Training Data.

IEEE transactions on medical imaging
In this work we reduce undersampling artefacts in two-dimensional (2D) golden-angle radial cine cardiac MRI by applying a modified version of the U-net. The network is trained on 2D spatio-temporal slices which are previously extracted from the image...

MRI super-resolution reconstruction for MRI-guided adaptive radiotherapy using cascaded deep learning: In the presence of limited training data and unknown translation model.

Medical physics
PURPOSE: Deep learning (DL)-based super-resolution (SR) reconstruction for magnetic resonance imaging (MRI) has recently been receiving attention due to the significant improvement in spatial resolution compared to conventional SR techniques. Challen...

Computer-aided detection and visualization of pulmonary embolism using a novel, compact, and discriminative image representation.

Medical image analysis
Diagnosing pulmonary embolism (PE) and excluding disorders that may clinically and radiologically simulate PE poses a challenging task for both human and machine perception. In this paper, we propose a novel vessel-oriented image representation (VOIR...

Fusing information from multiple 2D depth cameras for 3D human pose estimation in the operating room.

International journal of computer assisted radiology and surgery
PURPOSE: For many years, deep convolutional neural networks have achieved state-of-the-art results on a wide variety of computer vision tasks. 3D human pose estimation makes no exception and results on public benchmarks are impressive. However, speci...

Automated age estimation from MRI volumes of the hand.

Medical image analysis
Highly relevant for both clinical and legal medicine applications, the established radiological methods for estimating unknown age in children and adolescents are based on visual examination of bone ossification in X-ray images of the hand. Our group...

3D segmentation of nasopharyngeal carcinoma from CT images using cascade deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In the paper, we propose a new deep learning-based method for segmenting nasopharyngeal carcinoma (NPC) in the nasopharynx from three orthogonal CT images. The proposed method introduces a cascade strategy composed of two-phase manners. In CT images,...