AIMC Topic: Imaging, Three-Dimensional

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Fast fully automatic heart fat segmentation in computed tomography datasets.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Heart diseases affect a large part of the world's population. Studies have shown that these diseases are related to cardiac fat. Various medical diagnostic aid systems are developed to reduce these diseases. In this context, this paper presents a new...

Twin Robotic X-Ray System for 3D Cone-Beam CT of the Wrist: An Evaluation of Image Quality and Radiation Dose.

AJR. American journal of roentgenology
The purpose of this study was to assess image quality and radiation dose of a novel twin robotic x-ray system's 3D cone-beam CT (CBCT) function for the depiction of cadaveric wrists. Sixteen cadaveric wrists were scanned using dedicated low-dose an...

DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data.

Scientific reports
The scale of biological microscopy has increased dramatically over the past ten years, with the development of new modalities supporting collection of high-resolution fluorescence image volumes spanning hundreds of microns if not millimeters. The siz...

Liver tumor segmentation based on 3D convolutional neural network with dual scale.

Journal of applied clinical medical physics
PURPOSE: Liver is one of the organs with a high incidence of tumors in the human body. Malignant liver tumors seriously threaten human life and health. The difficulties of liver tumor segmentation from computed tomography (CT) image are: (a) The cont...

Recent Trends, Technical Concepts and Components of Computer-Assisted Orthopedic Surgery Systems: A Comprehensive Review.

Sensors (Basel, Switzerland)
Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation syst...

A manifold learning regularization approach to enhance 3D CT image-based lung nodule classification.

International journal of computer assisted radiology and surgery
PURPOSE: Diagnosis of lung cancer requires radiologists to review every lung nodule in CT images. Such a process can be very time-consuming, and the accuracy is affected by many factors, such as experience of radiologists and available diagnosis time...

Context-guided fully convolutional networks for joint craniomaxillofacial bone segmentation and landmark digitization.

Medical image analysis
Cone-beam computed tomography (CBCT) scans are commonly used in diagnosing and planning surgical or orthodontic treatment to correct craniomaxillofacial (CMF) deformities. Based on CBCT images, it is clinically essential to generate an accurate 3D mo...

3D nanostructural characterisation of grain boundaries in atom probe data utilising machine learning methods.

PloS one
Boosting is a family of supervised learning algorithm that convert a set of weak learners into a single strong one. It is popular in the field of object tracking, where its main purpose is to extract the position, motion, and trajectory from various ...

Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Precise 3D segmentation of infant brain tissues is an essential step towards comprehensive volumetric studies and quantitative analysis of early brain development. However, computing such segmentations is very challenging, especially for 6-month infa...

Deep learning segmentation of major vessels in X-ray coronary angiography.

Scientific reports
X-ray coronary angiography is a primary imaging technique for diagnosing coronary diseases. Although quantitative coronary angiography (QCA) provides morphological information of coronary arteries with objective quantitative measures, considerable tr...