AIMC Topic: Imaging, Three-Dimensional

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DDeep3M: Docker-powered deep learning for biomedical image segmentation.

Journal of neuroscience methods
BACKGROUND: Deep learning models are turning out to be increasingly popular in biomedical image processing. The fruitful utilization of these models, in most cases, is substantially restricted by the complicated configuration of computational environ...

Validating robotic couch isocentricity with 3D surface imaging.

Journal of applied clinical medical physics
BACKGROUND: A proton therapy system with 190° gantries uses robotic couch rotations to change the treatment beam laterality. Couch rotations are typically validated clinically with post-rotation radiographic imaging.

DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning.

Nature methods
An outstanding challenge in single-molecule localization microscopy is the accurate and precise localization of individual point emitters in three dimensions in densely labeled samples. One established approach for three-dimensional single-molecule l...

Toward automatic C-arm positioning for standard projections in orthopedic surgery.

International journal of computer assisted radiology and surgery
PURPOSE: Guidance and quality control in orthopedic surgery increasingly rely on intra-operative fluoroscopy using a mobile C-arm. The accurate acquisition of standardized and anatomy-specific projections is essential in this process. The correspondi...

Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework.

Methods (San Diego, Calif.)
The advancement of artificial intelligence concurrent with the development of medical imaging techniques provided a unique opportunity to turn medical imaging from mostly qualitative, to further quantitative and mineable data that can be explored for...

Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging.

NeuroImage
Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. To study these associations in more detail, it is highly important that the WM tracts can be accurately and reproducibly characterized fr...

Automatic post-stroke lesion segmentation on MR images using 3D residual convolutional neural network.

NeuroImage. Clinical
In this paper, we demonstrate the feasibility and performance of deep residual neural networks for volumetric segmentation of irreversibly damaged brain tissue lesions on T1-weighted MRI scans for chronic stroke patients. A total of 239 T1-weighted M...

Spatio-temporal deep learning methods for motion estimation using 4D OCT image data.

International journal of computer assisted radiology and surgery
PURPOSE: Localizing structures and estimating the motion of a specific target region are common problems for navigation during surgical interventions. Optical coherence tomography (OCT) is an imaging modality with a high spatial and temporal resoluti...

Recognition of Human Activities Using Depth Maps and the Viewpoint Feature Histogram Descriptor.

Sensors (Basel, Switzerland)
In this paper we propose a way of using depth maps transformed into 3D point clouds to classify human activities. The activities are described as time sequences of feature vectors based on the Viewpoint Feature Histogram descriptor (VFH) computed usi...

Predicting conversion to wet age-related macular degeneration using deep learning.

Nature medicine
Progression to exudative 'wet' age-related macular degeneration (exAMD) is a major cause of visual deterioration. In patients diagnosed with exAMD in one eye, we introduce an artificial intelligence (AI) system to predict progression to exAMD in the ...