AIMC Topic: Imaging, Three-Dimensional

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3D deeply supervised network for automated segmentation of volumetric medical images.

Medical image analysis
While deep convolutional neural networks (CNNs) have achieved remarkable success in 2D medical image segmentation, it is still a difficult task for CNNs to segment important organs or structures from 3D medical images owing to several mutually affect...

A machine-learning graph-based approach for 3D segmentation of Bruch's membrane opening from glaucomatous SD-OCT volumes.

Medical image analysis
Bruch's membrane opening-minimum rim width (BMO-MRW) is a recently proposed structural parameter which estimates the remaining nerve fiber bundles in the retina and is superior to other conventional structural parameters for diagnosing glaucoma. Meas...

Deep learning-based artificial vision for grasp classification in myoelectric hands.

Journal of neural engineering
OBJECTIVE: Computer vision-based assistive technology solutions can revolutionise the quality of care for people with sensorimotor disorders. The goal of this work was to enable trans-radial amputees to use a simple, yet efficient, computer vision sy...

VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images.

NeuroImage
Segmentation of key brain tissues from 3D medical images is of great significance for brain disease diagnosis, progression assessment and monitoring of neurologic conditions. While manual segmentation is time-consuming, laborious, and subjective, aut...

Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach.

NeuroImage
In this paper, we present a novel automated method for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a cascade of two 3D patch-wise convolutional neural networks (CNN). The first network is ...

Predicting 3D lip shapes using facial surface EMG.

PloS one
AIM: The aim of this study is to prove that facial surface electromyography (sEMG) conveys sufficient information to predict 3D lip shapes. High sEMG predictive accuracy implies we could train a neural control model for activation of biomechanical mo...

Learning a Deep Model for Human Action Recognition from Novel Viewpoints.

IEEE transactions on pattern analysis and machine intelligence
Recognizing human actions from unknown and unseen (novel) views is a challenging problem. We propose a Robust Non-Linear Knowledge Transfer Model (R-NKTM) for human action recognition from novel views. The proposed R-NKTM is a deep fully-connected ne...

A machine learning approach to investigate the relationship between shape features and numerically predicted risk of ascending aortic aneurysm.

Biomechanics and modeling in mechanobiology
Geometric features of the aorta are linked to patient risk of rupture in the clinical decision to electively repair an ascending aortic aneurysm (AsAA). Previous approaches have focused on relationship between intuitive geometric features (e.g., diam...

Endomicroscopy for Computer and Robot Assisted Intervention.

IEEE reviews in biomedical engineering
Endomicroscopy is a new technique that allows human tissue to be characterized in vivo and in situ, circumventing the need for conventional biopsy and histology. Despite increased application and growing research interests in this area, the clinical ...

MR-based synthetic CT generation using a deep convolutional neural network method.

Medical physics
PURPOSE: Interests have been rapidly growing in the field of radiotherapy to replace CT with magnetic resonance imaging (MRI), due to superior soft tissue contrast offered by MRI and the desire to reduce unnecessary radiation dose. MR-only radiothera...