Airway-tree segmentation in subjects with acute respiratory distress syndrome
International audience Acute respiratory distress syndrome (ARDS) is associated with a high mortality rate in intensive care units. To lower the number of fatal cases, it is necessary to customize the mechanical ventilator parameters according to the patient\textquoterights clinical condition. For t...
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HAL CCSD
2017
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Online Access: | https://hal.science/hal-01825334 https://hal.science/hal-01825334/document https://hal.science/hal-01825334/file/paper_revised.pdf https://doi.org/10.1007/978-3-319-59129-2_7 |
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ftunivstetienne:oai:HAL:hal-01825334v1 |
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Université Jean Monnet – Saint-Etienne: HAL |
op_collection_id |
ftunivstetienne |
language |
English |
topic |
[INFO.INFO-IM]Computer Science [cs]/Medical Imaging [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing |
spellingShingle |
[INFO.INFO-IM]Computer Science [cs]/Medical Imaging [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing Lidayová, K. Gómez Betancur, D.A. Frimmel, Hans Hoyos, Marcela, Hernández Orkisz, M. Smedby, Ö. Airway-tree segmentation in subjects with acute respiratory distress syndrome |
topic_facet |
[INFO.INFO-IM]Computer Science [cs]/Medical Imaging [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing |
description |
International audience Acute respiratory distress syndrome (ARDS) is associated with a high mortality rate in intensive care units. To lower the number of fatal cases, it is necessary to customize the mechanical ventilator parameters according to the patient\textquoterights clinical condition. For this, lung segmentation is required to assess aeration and alveolar recruitment. Airway segmentation may be used to reach a more accurate lung segmentation. In this paper, we seek to improve lung segmentation results by proposing a novel automatic airway-tree segmentation that is able to address the heterogeneity of ARDS pathology by handling various lung intensities differently. The method detects a simplified airway skeleton, thereby obtains a set of seed points together with an approximate radius and intensity range related to each of the points. These seeds are the input for an onion-kernel region-growing segmentation algorithm where knowledge about radius and intensity range restricts the possible leakage in the parenchyma. The method was evaluated qualitatively on 70 thoracic Computed Tomography volumes of subjects with ARDS, acquired at significantly different mechanical ventilation conditions. It found a large proportion of airway branches including tiny poorly-aerated bronchi. Quantitative evaluation was performed indirectly and showed that the resulting airway segmentation provides important anatomic landmarks. Their correspondences are needed to help a registration-based segmentation of the lungs in difficult ARDS cases where the lung boundary contrast is completely missing. The proposed method takes an average time of 43s to process a thoracic volume which is valuable for the clinical use. |
author2 |
Grupo IMAGINE Universidad de los Andes Bogota (UNIANDES) Imagerie et modélisation Vasculaires, Thoraciques et Cérébrales (MOTIVATE) Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS) Université Claude Bernard Lyon 1 (UCBL) Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon) Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL) Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS) Department of Medical and Health Sciences (IMH) Linköping University (LIU) School of Technology and Health KTH Royal Institute of Technology Stockholm (KTH ) |
format |
Conference Object |
author |
Lidayová, K. Gómez Betancur, D.A. Frimmel, Hans Hoyos, Marcela, Hernández Orkisz, M. Smedby, Ö. |
author_facet |
Lidayová, K. Gómez Betancur, D.A. Frimmel, Hans Hoyos, Marcela, Hernández Orkisz, M. Smedby, Ö. |
author_sort |
Lidayová, K. |
title |
Airway-tree segmentation in subjects with acute respiratory distress syndrome |
title_short |
Airway-tree segmentation in subjects with acute respiratory distress syndrome |
title_full |
Airway-tree segmentation in subjects with acute respiratory distress syndrome |
title_fullStr |
Airway-tree segmentation in subjects with acute respiratory distress syndrome |
title_full_unstemmed |
Airway-tree segmentation in subjects with acute respiratory distress syndrome |
title_sort |
airway-tree segmentation in subjects with acute respiratory distress syndrome |
publisher |
HAL CCSD |
publishDate |
2017 |
url |
https://hal.science/hal-01825334 https://hal.science/hal-01825334/document https://hal.science/hal-01825334/file/paper_revised.pdf https://doi.org/10.1007/978-3-319-59129-2_7 |
op_coverage |
Tromsø, Norway |
genre |
Tromsø |
genre_facet |
Tromsø |
op_source |
Lecture Notes in Computer Science Scandinavian Conference on Image Analysis https://hal.science/hal-01825334 Scandinavian Conference on Image Analysis, 2017, Tromsø, Norway. pp.76-87, ⟨10.1007/978-3-319-59129-2_7⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-59129-2_7 hal-01825334 https://hal.science/hal-01825334 https://hal.science/hal-01825334/document https://hal.science/hal-01825334/file/paper_revised.pdf doi:10.1007/978-3-319-59129-2_7 |
op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.1007/978-3-319-59129-2_7 |
container_start_page |
76 |
op_container_end_page |
87 |
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1810483824221487104 |
spelling |
ftunivstetienne:oai:HAL:hal-01825334v1 2024-09-15T18:39:27+00:00 Airway-tree segmentation in subjects with acute respiratory distress syndrome Lidayová, K. Gómez Betancur, D.A. Frimmel, Hans Hoyos, Marcela, Hernández Orkisz, M. Smedby, Ö. Grupo IMAGINE Universidad de los Andes Bogota (UNIANDES) Imagerie et modélisation Vasculaires, Thoraciques et Cérébrales (MOTIVATE) Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS) Université Claude Bernard Lyon 1 (UCBL) Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon) Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL) Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS) Department of Medical and Health Sciences (IMH) Linköping University (LIU) School of Technology and Health KTH Royal Institute of Technology Stockholm (KTH ) Tromsø, Norway 2017 https://hal.science/hal-01825334 https://hal.science/hal-01825334/document https://hal.science/hal-01825334/file/paper_revised.pdf https://doi.org/10.1007/978-3-319-59129-2_7 en eng HAL CCSD Springer International Publishing info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-59129-2_7 hal-01825334 https://hal.science/hal-01825334 https://hal.science/hal-01825334/document https://hal.science/hal-01825334/file/paper_revised.pdf doi:10.1007/978-3-319-59129-2_7 info:eu-repo/semantics/OpenAccess Lecture Notes in Computer Science Scandinavian Conference on Image Analysis https://hal.science/hal-01825334 Scandinavian Conference on Image Analysis, 2017, Tromsø, Norway. pp.76-87, ⟨10.1007/978-3-319-59129-2_7⟩ [INFO.INFO-IM]Computer Science [cs]/Medical Imaging [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing info:eu-repo/semantics/conferenceObject Conference papers 2017 ftunivstetienne https://doi.org/10.1007/978-3-319-59129-2_7 2024-07-09T00:05:35Z International audience Acute respiratory distress syndrome (ARDS) is associated with a high mortality rate in intensive care units. To lower the number of fatal cases, it is necessary to customize the mechanical ventilator parameters according to the patient\textquoterights clinical condition. For this, lung segmentation is required to assess aeration and alveolar recruitment. Airway segmentation may be used to reach a more accurate lung segmentation. In this paper, we seek to improve lung segmentation results by proposing a novel automatic airway-tree segmentation that is able to address the heterogeneity of ARDS pathology by handling various lung intensities differently. The method detects a simplified airway skeleton, thereby obtains a set of seed points together with an approximate radius and intensity range related to each of the points. These seeds are the input for an onion-kernel region-growing segmentation algorithm where knowledge about radius and intensity range restricts the possible leakage in the parenchyma. The method was evaluated qualitatively on 70 thoracic Computed Tomography volumes of subjects with ARDS, acquired at significantly different mechanical ventilation conditions. It found a large proportion of airway branches including tiny poorly-aerated bronchi. Quantitative evaluation was performed indirectly and showed that the resulting airway segmentation provides important anatomic landmarks. Their correspondences are needed to help a registration-based segmentation of the lungs in difficult ARDS cases where the lung boundary contrast is completely missing. The proposed method takes an average time of 43s to process a thoracic volume which is valuable for the clinical use. Conference Object Tromsø Université Jean Monnet – Saint-Etienne: HAL 76 87 |