AIMC Topic: Imaging, Three-Dimensional

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3D Deep Learning Angiography (3D-DLA) from C-arm Conebeam CT.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Deep learning is a branch of artificial intelligence that has demonstrated unprecedented performance in many medical imaging applications. Our purpose was to develop a deep learning angiography method to generate 3D cerebral a...

Deep Spatiality: Unsupervised Learning of Spatially-Enhanced Global and Local 3D Features by Deep Neural Network with Coupled Softmax.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
The discriminability of Bag-of-Words representations can be increased via encoding the spatial relationship among virtual words on 3D shapes. However, this encoding task involves several issues, including arbitrary mesh resolutions, irregular vertex ...

An application of cascaded 3D fully convolutional networks for medical image segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of volumetric images. In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical st...

Social Robotics in Therapy of Apraxia of Speech.

Journal of healthcare engineering
Apraxia of speech is a motor speech disorder in which messages from the brain to the mouth are disrupted, resulting in an inability for moving lips or tongue to the right place to pronounce sounds correctly. Current therapies for this condition invol...

A deep learning approach for pose estimation from volumetric OCT data.

Medical image analysis
Tracking the pose of instruments is a central problem in image-guided surgery. For microscopic scenarios, optical coherence tomography (OCT) is increasingly used as an imaging modality. OCT is suitable for accurate pose estimation due to its micromet...

Use the force: deformation correction in robotic 3D ultrasound.

International journal of computer assisted radiology and surgery
PURPOSE: Ultrasound acquisitions are typically affected by deformations due to the pressure applied onto the contact surface. While a certain amount of pressure is necessary to ensure good acoustic coupling and visibility of the anatomy under examina...

Multi-channel multi-scale fully convolutional network for 3D perivascular spaces segmentation in 7T MR images.

Medical image analysis
Accurate segmentation of perivascular spaces (PVSs) is an important step for quantitative study of PVS morphology. However, since PVSs are the thin tubular structures with relatively low contrast and also the number of PVSs is often large, it is chal...

Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning.

Medical image analysis
Methods for aligning 3D fetal neurosonography images must be robust to (i) intensity variations, (ii) anatomical and age-specific differences within the fetal population, and (iii) the variations in fetal position. To this end, we propose a multi-tas...

Isotropic Reconstruction of MR Images Using 3D Patch-Based Self-Similarity Learning.

IEEE transactions on medical imaging
Isotropic three-dimensional (3D) acquisition is a challenging task in magnetic resonance imaging (MRI). Particularly in cardiac MRI, due to hardware and time limitations, current 3D acquisitions are limited by low-resolution, especially in the throug...

Kidney Detection in 3-D Ultrasound Imagery via Shape-to-Volume Registration Based on Spatially Aligned Neural Network.

IEEE journal of biomedical and health informatics
This paper introduces a computer-aided kidney shape detection method suitable for volumetric (3D) ultrasound images. Using shape and texture priors, the proposed method automates the process of kidney detection, which is a problem of great importance...