A machine learning pipeline for internal anatomical landmark embedding based on a patient surface model.

08:00 EDT 13th October 2018 | BioPortfolio

Summary of "A machine learning pipeline for internal anatomical landmark embedding based on a patient surface model."

With the recent introduction of fully assisting scanner technologies by Siemens Healthineers (Erlangen, Germany), a patient surface model was introduced to the diagnostic imaging device market. Such a patient representation can be used to automate and accelerate the clinical imaging workflow, manage patient dose, and provide navigation assistance for computed tomography diagnostic imaging. In addition to diagnostic imaging, a patient surface model has also tremendous potential to simplify interventional imaging. For example, if the anatomy of a patient was known, a robotic angiography system could be automatically positioned such that the organ of interest is positioned in the system's iso-center offering a good and flexible view on the underlying patient anatomy quickly and without any additional X-ray dose.


Journal Details

This article was published in the following journal.

Name: International journal of computer assisted radiology and surgery
ISSN: 1861-6429


DeepDyve research library

PubMed Articles [20042 Associated PubMed Articles listed on BioPortfolio]

Automatic Normalization of Anatomical Phrases in Radiology Reports Using Unsupervised Learning.

In today's radiology workflow, free-text reporting is established as the most common medium to capture, store, and communicate clinical information. Radiologists routinely refer to prior radiology rep...

Stochastic Graphlet Embedding.

Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semistructured data as graphs where nodes correspond to primitives (par...

High throughput automated detection of axial malformations in Medaka embryo.

Fish embryo models are widely used as screening tools to assess the efficacy and/or toxicity of chemicals. This assessment involves the analysis of embryo morphological abnormalities. In this article,...

RuleMatrix: Visualizing and Understanding Classifiers with Rules.

With the growing adoption of machine learning techniques, there is a surge of research interest towards making machine learning systems more transparent and interpretable. Various visualizations have ...

Functional brain networks and neuroanatomy underpinning nausea severity can predict nausea susceptibility using machine learning.

Nausea is an adverse experience characterised by alterations in autonomic and cerebral function. Susceptibility to nausea is difficult to predict, but machine learning has yet to be applied to this fi...

Clinical Trials [5345 Associated Clinical Trials listed on BioPortfolio]

Association Analysis and Medical Machine Intelligence Study for Traditional Chinese Internal Medicine

This study aimed at 1. exploring the relationships of traditional Chinese medicine (TCM) syndrome and internal diseases, 2. investigating the relationships of TCM treatment and internal di...

A Clinical Trial of Thread-embedding Therapy at Acupuncture Point for Simple Obesity

Thread-embedding Therapy has been used for treating Obesity in recent years. This research is aimed to observe the clinical effect of thread-embedding therapy in treating Simple Obesity. O...

Machine Learning From Fetal Flow Waveforms to Predict Adverse Perinatal Outcomes

The aim of this study is to get a proof of concept for using a computational model of fetal haemodynamics, combined with machine learning based on Doppler patterns of the fetal cardiovascu...

Machine Learning-Based Risk Profile Classification of Patients Undergoing Elective Heart Valve Surgery

Machine learning methods potentially provide a highly accurate and detailed assessment of expected individual patient risk before elective cardiac surgery. Correct anticipation of this ris...

Clinical Research on the Efficacy of Thread-embedding Acupuncture on Herniated Intervertebral Disc of Lumbar Spine

This clinical trial is designed to evaluate the efficacy and safety of thread-embedding acupuncture on lumbar herniated intervertebral disc (L-HIVD) by assessing pain, function, and qualit...

Medical and Biotech [MESH] Definitions

A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of unlabeled paired input-output training (sample) data.

A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.

SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples.

A type of ARTIFICIAL INTELLIGENCE that enable COMPUTERS to independently initiate and execute LEARNING when exposed to new data.

Process in which individuals take the initiative, in diagnosing their learning needs, formulating learning goals, identifying resources for learning, choosing and implementing learning strategies and evaluating learning outcomes (Knowles, 1975)

Quick Search


DeepDyve research library

Relevant Topic

Radiology is the branch of medicine that studies imaging of the body; X-ray (basic, angiography, barium swallows), ultrasound, MRI, CT and PET. These imaging techniques can be used to diagnose, but also to treat a range of conditions, by allowing visuali...

Searches Linking to this Article