Airway-tree segmentation in subjects with acute respiratory distress syndrome

• Main Authors: Lidayová, K., Gómez Betancur, D.A., Frimmel, Hans, Hoyos, Marcela, Hernández, Orkisz, M., Smedby, Ö.
• Other Authors: 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
• Language: English
• Published: HAL CCSD 2017
• Subjects: [INFO.INFO-IM]Computer Science [cs]/Medical Imaging; [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing; Norway; Tromsø
• 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

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.