AIMC Topic: Imaging, Three-Dimensional

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Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning.

IEEE transactions on medical imaging
We describe an automated methodology for the analysis of unregistered cranio-caudal (CC) and medio-lateral oblique (MLO) mammography views in order to estimate the patient's risk of developing breast cancer. The main innovation behind this methodolog...

Machine learning techniques for the optimization of joint replacements: Application to a short-stem hip implant.

PloS one
Today, different implant designs exist in the market; however, there is not a clear understanding of which are the best implant design parameters to achieve mechanical optimal conditions. Therefore, the aim of this project was to investigate if the g...

Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method.

Medical physics
PURPOSE: We propose a single network trained by pixel-to-label deep learning to address the general issue of automatic multiple organ segmentation in three-dimensional (3D) computed tomography (CT) images. Our method can be described as a voxel-wise ...

3D Reconstruction of human bones based on dictionary learning.

Medical engineering & physics
An effective method for reconstructing a 3D model of human bones from computed tomography (CT) image data based on dictionary learning is proposed. In this study, the dictionary comprises the vertices of triangular meshes, and the sparse coefficient ...

An expert system feedback tool improves the reliability of clinical gait kinematics for older adults with lower limb osteoarthritis.

Gait & posture
Recently, an expert system was developed to provide feedback to examiners with the aim of improving reliability of marker-based gait analysis. The purpose of the current study was to evaluate the effectiveness of this novel feedback tool in improving...

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.

PloS one
Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural hete...

Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging.

Magnetic resonance in medicine
PURPOSE: To describe and evaluate a new fully automated musculoskeletal tissue segmentation method using deep convolutional neural network (CNN) and three-dimensional (3D) simplex deformable modeling to improve the accuracy and efficiency of cartilag...

Low-Grade Glioma Segmentation Based on CNN with Fully Connected CRF.

Journal of healthcare engineering
This work proposed a novel automatic three-dimensional (3D) magnetic resonance imaging (MRI) segmentation method which would be widely used in the clinical diagnosis of the most common and aggressive brain tumor, namely, glioma. The method combined a...

Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks.

NeuroImage. Clinical
Stroke is an acute cerebral vascular disease, which is likely to cause long-term disabilities and death. Acute ischemic lesions occur in most stroke patients. These lesions are treatable under accurate diagnosis and treatments. Although diffusion-wei...

Deep monocular 3D reconstruction for assisted navigation in bronchoscopy.

International journal of computer assisted radiology and surgery
PURPOSE: In bronchoschopy, computer vision systems for navigation assistance are an attractive low-cost solution to guide the endoscopist to target peripheral lesions for biopsy and histological analysis. We propose a decoupled deep learning architec...