Article

Automated MRI Lung Segmentation and 3D Morphological Features for Quantification of Neonatal Lung Disease

2021-08-08

Abstract excerpt

<h4>ABSTRACT</h4> The diagnosis of neonatal respiratory diseases is currently based on clinical criteria. However, lung structural information is generally lacking due to the unavailability of routinely applicable, radiation-free imaging tools as well as the time-consuming, often non-standardized manual analysis of imaging data. Increased efficiency, comparability and accuracy in image quantification is needed in...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 074efed1-fc35-5561-86a0-3b13f04c637b
DOI: 10.1101/2021.08.06.21261648

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Automated MRI Lung Segmentation and 3D Morphological Features for Quantification of Neonatal Lung DiseaseDOI 10.1101/2021.08.06.21261648Read

Towards automated multi-regional lung parcellation for 0.55-3T 3D T2w fetal MRISemantic proximity Read

BOston Neonatal Brain Injury Dataset for Hypoxic Ischemic Encephalopathy (BONBID-HIE): Part I. MRI and Manual Lesion AnnotationSemantic proximity Read

Automated assessment of neonatal internal capsule maturation on T2-weighted MRI across 7T and 3TSemantic proximity Read

Application of Deep Learning-Based Artificial Intelligence Model in Lung Ultrasound for Pediatric Lobar PneumoniaSemantic proximity Read

Convolutional Automatic Identification of B-lines and Interstitial Syndrome in Lung Ultrasound Images Using Pre-Trained Neural Networks with Feature FusionSemantic proximity Read

Deep learning microstructure estimation of developing brains from diffusion MRI: a newborn and fetal studySemantic proximity Read

Automated Brain and CSF Volume Assessment in Infant Hydrocephalus Using Deep LearningSemantic proximity Read

3D T2w fetal body MRI: automated organ volumetry, growth charts and population-averaged atlasSemantic proximity Read

A common neonatal image phenotype predicts adverse neurodevelopmental outcome in children born preterm.Semantic proximity Read

A Seven-Component Anatomy-Guided Literature Review Framework for Chronic Respiratory Disease and Medical ImagingSemantic proximity Read

Mapping fetal brain development based on automated segmentation and 4D brain atlasingSemantic proximity Read

Virtual Growing Child (VGC): A general normative comparative system via quantitative dynamic MRI for quantifying pediatric regional respiratory anomalies with application in thoracic insufficiency syndrome (TIS)Semantic proximity Read