Airway-Tree Segmentation in Subjects with Acute Respiratory Distress Syndrome

  • Kristína Lidayová
  • Duván Alberto Gómez Betancur
  • Hans Frimmel
  • Marcela Hernández Hoyos
  • Maciej Orkisz
  • Örjan Smedby
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10270)

Abstract

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’s 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 43 s to process a thoracic volume which is valuable for the clinical use.

Keywords

Airway segmentation Airway-tree centerline detection Thoracic CT ARDS 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Kristína Lidayová
    • 1
  • Duván Alberto Gómez Betancur
    • 4
  • Hans Frimmel
    • 2
  • Marcela Hernández Hoyos
    • 4
  • Maciej Orkisz
    • 5
  • Örjan Smedby
    • 3
  1. 1.Division of Visual Information and Interaction, Centre for Image AnalysisUppsala UniversityUppsalaSweden
  2. 2.Division of Scientific Computing, Department of Information TechnologyUppsala UniversityUppsalaSweden
  3. 3.School of Technology and HealthKTH Royal Institute of TechnologyStockholmSweden
  4. 4.Systems and Computing Engineering Department, School of EngineeringUniversidad de Los AndesBogotáColombia
  5. 5.CREATIS UMR 5220, U1206, CNRSInserm, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, Univ. Lyon, 69621LyonFrance

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