AIMC Topic: Imaging, Three-Dimensional

Clear Filters Showing 1111 to 1120 of 1894 articles

Automated classification of three-dimensional reconstructions of coral reefs using convolutional neural networks.

PloS one
Coral reefs are biologically diverse and structurally complex ecosystems, which have been severally affected by human actions. Consequently, there is a need for rapid ecological assessment of coral reefs, but current approaches require time consuming...

Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI.

IEEE journal of biomedical and health informatics
Brain Metastases (BM) complicate 20-40% of cancer cases. BM lesions can present as punctate (1 mm) foci, requiring high-precision Magnetic Resonance Imaging (MRI) in order to prevent inadequate or delayed BM treatment. However, BM lesion detection re...

Physics-guided machine learning for 3-D quantitative quasi-static elasticity imaging.

Physics in medicine and biology
We present a 3D extension of the Autoprogressive Method (AutoP) for quantitative quasi-static ultrasonic elastography (QUSE) based on sparse sampling of force-displacement measurements. Compared to current model-based inverse methods, our approach re...

Computer-aided Detection of Brain Metastases in T1-weighted MRI for Stereotactic Radiosurgery Using Deep Learning Single-Shot Detectors.

Radiology
Background Brain metastases are manually identified during stereotactic radiosurgery (SRS) treatment planning, which is time consuming and potentially challenging. Purpose To develop and investigate deep learning (DL) methods for detecting brain meta...

Novel body fat estimation using machine learning and 3-dimensional optical imaging.

European journal of clinical nutrition
Estimates of body composition have been derived using 3-dimensional optical imaging (3DO), but no equations to date have been calibrated using a 4-component (4C) model criterion. This investigation reports the development of a novel body fat predicti...

Machine learning analysis of whole mouse brain vasculature.

Nature methods
Tissue clearing methods enable the imaging of biological specimens without sectioning. However, reliable and scalable analysis of large imaging datasets in three dimensions remains a challenge. Here we developed a deep learning-based framework to qua...

AdaEn-Net: An ensemble of adaptive 2D-3D Fully Convolutional Networks for medical image segmentation.

Neural networks : the official journal of the International Neural Network Society
Fully Convolutional Networks (FCNs) have emerged as powerful segmentation models but are usually designed manually, which requires extensive time and can result in large and complex architectures. There is a growing interest to automatically design e...

An integrated deep learning framework for joint segmentation of blood pool and myocardium.

Medical image analysis
Simultaneous and automatic segmentation of the blood pool and myocardium is an important precondition for early diagnosis and pre-operative planning in patients with complex congenital heart disease. However, due to the high diversity of cardiovascul...