Cargando…

Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network

Pericardial effusions (PEFs) are often missed on Computed Tomography (CT), which particularly affects the outcome of patients presenting with hemodynamic compromise. An automatic PEF detection, segmentation, and classification tool would expedite and improve CT based PEF diagnosis; 258 CTs with (206...

Descripción completa

Detalles Bibliográficos
Autores principales: Wilder-Smith, Adrian Jonathan, Yang, Shan, Weikert, Thomas, Bremerich, Jens, Haaf, Philip, Segeroth, Martin, Ebert, Lars C., Sauter, Alexander, Sexauer, Raphael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9139725/
https://www.ncbi.nlm.nih.gov/pubmed/35626201
http://dx.doi.org/10.3390/diagnostics12051045
_version_ 1784714925523861504
author Wilder-Smith, Adrian Jonathan
Yang, Shan
Weikert, Thomas
Bremerich, Jens
Haaf, Philip
Segeroth, Martin
Ebert, Lars C.
Sauter, Alexander
Sexauer, Raphael
author_facet Wilder-Smith, Adrian Jonathan
Yang, Shan
Weikert, Thomas
Bremerich, Jens
Haaf, Philip
Segeroth, Martin
Ebert, Lars C.
Sauter, Alexander
Sexauer, Raphael
author_sort Wilder-Smith, Adrian Jonathan
collection PubMed
description Pericardial effusions (PEFs) are often missed on Computed Tomography (CT), which particularly affects the outcome of patients presenting with hemodynamic compromise. An automatic PEF detection, segmentation, and classification tool would expedite and improve CT based PEF diagnosis; 258 CTs with (206 with simple PEF, 52 with hemopericardium) and without PEF (each 134 with contrast, 124 non-enhanced) were identified using the radiology report (01/2016–01/2021). PEF were manually 3D-segmented. A deep convolutional neural network (nnU-Net) was trained on 316 cases and separately tested on the remaining 200 and 22 external post-mortem CTs. Inter-reader variability was tested on 40 CTs. PEF classification utilized the median Hounsfield unit from each prediction. The sensitivity and specificity for PEF detection was 97% (95% CI 91.48–99.38%) and 100.00% (95% CI 96.38–100.00%) and 89.74% and 83.61% for diagnosing hemopericardium (AUC 0.944, 95% CI 0.904–0.984). Model performance (Dice coefficient: 0.75 ± 0.01) was non-inferior to inter-reader (0.69 ± 0.02) and was unaffected by contrast administration nor alternative chest pathology (p > 0.05). External dataset testing yielded similar results. Our model reliably detects, segments, and classifies PEF on CT in a complex dataset, potentially serving as an alert tool whilst enhancing report quality. The model and corresponding datasets are publicly available.
format Online
Article
Text
id pubmed-9139725
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-91397252022-05-28 Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network Wilder-Smith, Adrian Jonathan Yang, Shan Weikert, Thomas Bremerich, Jens Haaf, Philip Segeroth, Martin Ebert, Lars C. Sauter, Alexander Sexauer, Raphael Diagnostics (Basel) Article Pericardial effusions (PEFs) are often missed on Computed Tomography (CT), which particularly affects the outcome of patients presenting with hemodynamic compromise. An automatic PEF detection, segmentation, and classification tool would expedite and improve CT based PEF diagnosis; 258 CTs with (206 with simple PEF, 52 with hemopericardium) and without PEF (each 134 with contrast, 124 non-enhanced) were identified using the radiology report (01/2016–01/2021). PEF were manually 3D-segmented. A deep convolutional neural network (nnU-Net) was trained on 316 cases and separately tested on the remaining 200 and 22 external post-mortem CTs. Inter-reader variability was tested on 40 CTs. PEF classification utilized the median Hounsfield unit from each prediction. The sensitivity and specificity for PEF detection was 97% (95% CI 91.48–99.38%) and 100.00% (95% CI 96.38–100.00%) and 89.74% and 83.61% for diagnosing hemopericardium (AUC 0.944, 95% CI 0.904–0.984). Model performance (Dice coefficient: 0.75 ± 0.01) was non-inferior to inter-reader (0.69 ± 0.02) and was unaffected by contrast administration nor alternative chest pathology (p > 0.05). External dataset testing yielded similar results. Our model reliably detects, segments, and classifies PEF on CT in a complex dataset, potentially serving as an alert tool whilst enhancing report quality. The model and corresponding datasets are publicly available. MDPI 2022-04-21 /pmc/articles/PMC9139725/ /pubmed/35626201 http://dx.doi.org/10.3390/diagnostics12051045 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wilder-Smith, Adrian Jonathan
Yang, Shan
Weikert, Thomas
Bremerich, Jens
Haaf, Philip
Segeroth, Martin
Ebert, Lars C.
Sauter, Alexander
Sexauer, Raphael
Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network
title Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network
title_full Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network
title_fullStr Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network
title_full_unstemmed Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network
title_short Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network
title_sort automated detection, segmentation, and classification of pericardial effusions on chest ct using a deep convolutional neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9139725/
https://www.ncbi.nlm.nih.gov/pubmed/35626201
http://dx.doi.org/10.3390/diagnostics12051045
work_keys_str_mv AT wildersmithadrianjonathan automateddetectionsegmentationandclassificationofpericardialeffusionsonchestctusingadeepconvolutionalneuralnetwork
AT yangshan automateddetectionsegmentationandclassificationofpericardialeffusionsonchestctusingadeepconvolutionalneuralnetwork
AT weikertthomas automateddetectionsegmentationandclassificationofpericardialeffusionsonchestctusingadeepconvolutionalneuralnetwork
AT bremerichjens automateddetectionsegmentationandclassificationofpericardialeffusionsonchestctusingadeepconvolutionalneuralnetwork
AT haafphilip automateddetectionsegmentationandclassificationofpericardialeffusionsonchestctusingadeepconvolutionalneuralnetwork
AT segerothmartin automateddetectionsegmentationandclassificationofpericardialeffusionsonchestctusingadeepconvolutionalneuralnetwork
AT ebertlarsc automateddetectionsegmentationandclassificationofpericardialeffusionsonchestctusingadeepconvolutionalneuralnetwork
AT sauteralexander automateddetectionsegmentationandclassificationofpericardialeffusionsonchestctusingadeepconvolutionalneuralnetwork
AT sexauerraphael automateddetectionsegmentationandclassificationofpericardialeffusionsonchestctusingadeepconvolutionalneuralnetwork