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Airway Tree

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Marleen De Bruijne – One of the best experts on this subject based on the ideXlab platform.

  • An Airway Tree-shape Model for Geodesic Airway Branch Labeling
    , 2011
    Co-Authors: Aasa Feragen, Joseph M. Reinhardt, Lo Pechin, Vladlena Gorbunova, Mads Nielsen, Asger Dirksen, François Lauze, Marleen De Bruijne

    Abstract:

    We present a mathematical Airway Tree-shape framework where Airway Trees are compared using geodesic distances. The framework consists of a rigorously de ned shape space for Treelike shapes, endowed with a metric such that the shape space is a geodesic metric space. This means that the distance between two Tree-shapes can be realized as the length of the geodesic, or shortest deformation, connecting the two shapes. By computing geodesics between Airway Trees, as well as the corresponding Airway deformation, we generate Airway branch correspondences. Correspondences between an unlabeled Airway Tree and a set of labeled Airway Trees are combined with a voting scheme to perform automatic branch labeling of segmented Airways from the challenging EXACT’09 test set. In spite of the varying quality of the data, we obtain robust labeling results.

  • vessel guided Airway Tree segmentation a voxel classification approach
    Medical Image Analysis, 2010
    Co-Authors: Jon Sporring, Marleen De Bruijne, Haseem Ashraf, Jesper Holst Pedersen

    Abstract:

    This paper presents a method for Airway Tree segmentation that uses a combination of a trained Airway appearance model, vessel and Airway orientation information, and region growing. We propose a voxel classification approach for the appearance model, which uses a classifier that is trained to differentiate between Airway and non-Airway voxels. This is in contrast to previous works that use either intensity alone or hand crafted models of Airway appearance. We show that the appearance model can be trained with a set of easily acquired, incomplete, Airway Tree segmentations. A vessel orientation similarity measure is introduced, which indicates how similar the orientation of an Airway candidate is to the orientation of the neighboring vessel. We use this vessel orientation similarity measure to overcome regions in the Airway Tree that have a low response from the appearance model. The proposed method is evaluated on 250 low dose computed tomography images from a lung cancer screening trial. Our experiments showed that applying the region growing algorithm on the Airway appearance model produces more complete Airway segmentations, leading to on average 20% longer Trees, and 50% less leakage. When combining the Airway appearance model with vessel orientation similarity, the improvement is even more significant (p<0.01) than only using the Airway appearance model, with on average 7% increase in the total length of branches extracted correctly.

  • Vessel-guided Airway Tree segmentation: A voxel classification approach
    Medical image analysis, 2010
    Co-Authors: Jon Sporring, Haseem Ashraf, Jesper Holst Pedersen, Marleen De Bruijne

    Abstract:

    This paper presents a method for Airway Tree segmentation that uses a combination of a trained Airway appearance model, vessel and Airway orientation information, and region growing. We propose a voxel classification approach for the appearance model, which uses a classifier that is trained to differentiate between Airway and non-Airway voxels. This is in contrast to previous works that use either intensity alone or hand crafted models of Airway appearance. We show that the appearance model can be trained with a set of easily acquired, incomplete, Airway Tree segmentations. A vessel orientation similarity measure is introduced, which indicates how similar the orientation of an Airway candidate is to the orientation of the neighboring vessel. We use this vessel orientation similarity measure to overcome regions in the Airway Tree that have a low response from the appearance model. The proposed method is evaluated on 250 low dose computed tomography images from a lung cancer screening trial. Our experiments showed that applying the region growing algorithm on the Airway appearance model produces more complete Airway segmentations, leading to on average 20% longer Trees, and 50% less leakage. When combining the Airway appearance model with vessel orientation similarity, the improvement is even more significant (p

Jon Sporring – One of the best experts on this subject based on the ideXlab platform.

  • vessel guided Airway Tree segmentation a voxel classification approach
    Medical Image Analysis, 2010
    Co-Authors: Jon Sporring, Marleen De Bruijne, Haseem Ashraf, Jesper Holst Pedersen

    Abstract:

    This paper presents a method for Airway Tree segmentation that uses a combination of a trained Airway appearance model, vessel and Airway orientation information, and region growing. We propose a voxel classification approach for the appearance model, which uses a classifier that is trained to differentiate between Airway and non-Airway voxels. This is in contrast to previous works that use either intensity alone or hand crafted models of Airway appearance. We show that the appearance model can be trained with a set of easily acquired, incomplete, Airway Tree segmentations. A vessel orientation similarity measure is introduced, which indicates how similar the orientation of an Airway candidate is to the orientation of the neighboring vessel. We use this vessel orientation similarity measure to overcome regions in the Airway Tree that have a low response from the appearance model. The proposed method is evaluated on 250 low dose computed tomography images from a lung cancer screening trial. Our experiments showed that applying the region growing algorithm on the Airway appearance model produces more complete Airway segmentations, leading to on average 20% longer Trees, and 50% less leakage. When combining the Airway appearance model with vessel orientation similarity, the improvement is even more significant (p<0.01) than only using the Airway appearance model, with on average 7% increase in the total length of branches extracted correctly.

  • Vessel-guided Airway Tree segmentation: A voxel classification approach
    Medical image analysis, 2010
    Co-Authors: Jon Sporring, Haseem Ashraf, Jesper Holst Pedersen, Marleen De Bruijne

    Abstract:

    This paper presents a method for Airway Tree segmentation that uses a combination of a trained Airway appearance model, vessel and Airway orientation information, and region growing. We propose a voxel classification approach for the appearance model, which uses a classifier that is trained to differentiate between Airway and non-Airway voxels. This is in contrast to previous works that use either intensity alone or hand crafted models of Airway appearance. We show that the appearance model can be trained with a set of easily acquired, incomplete, Airway Tree segmentations. A vessel orientation similarity measure is introduced, which indicates how similar the orientation of an Airway candidate is to the orientation of the neighboring vessel. We use this vessel orientation similarity measure to overcome regions in the Airway Tree that have a low response from the appearance model. The proposed method is evaluated on 250 low dose computed tomography images from a lung cancer screening trial. Our experiments showed that applying the region growing algorithm on the Airway appearance model produces more complete Airway segmentations, leading to on average 20% longer Trees, and 50% less leakage. When combining the Airway appearance model with vessel orientation similarity, the improvement is even more significant (p

  • Airway Tree extraction with locally optimal paths
    Medical Image Computing and Computer-Assisted Intervention, 2009
    Co-Authors: Jon Sporring, Jesper Johannes Pedersen, Marleen De Bruijne

    Abstract:

    This paper proposes a method to extract the Airway Tree from CT images by continually extending the Tree with locally optimal paths. This is in contrast to commonly used region growing based approaches that only search the space of the immediate neighbors. The result is a much more robust method for Tree extraction that can overcome local occlusions. The cost function for obtaining the optimal paths takes into account of an Airway probability map as well as measures of Airway shape and orientation derived from multi-scale Hessian eigen analysis on the Airway probability. Significant improvements were achieved compared to a region growing based method, with up to 36% longer Trees at a slight increase of false positive rate.

Bram Van Ginneken – One of the best experts on this subject based on the ideXlab platform.

  • Extraction of Airways from CT (EXACT’09).
    IEEE Transactions on Medical Imaging, 2012
    Co-Authors: Bram Van Ginneken, Joseph M. Reinhardt, Romulo Pinho, Jan Sijbers, Benjamin Irving, Tarunashree Yavarna, Pim A. De Jong, Catalin Fetita, Margarete Ortner, Marco Feuerstein

    Abstract:

    This paper describes a framework for establishing a reference Airway Tree segmentation, which was used to quantitatively evaluate fifteen different Airway Tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented Airway Tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the Airway Tree. Finally, the reference Airway Trees are constructed by taking the union of all correctly extracted branch segments. Fifteen Airway Tree extraction algorithms from different research groups are evaluated on a diverse set of twenty chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in Airway segmentation algorithms.

  • Extraction of Airways From CT (EXACT’09)
    IEEE transactions on medical imaging, 2012
    Co-Authors: Bram Van Ginneken, Joseph M. Reinhardt, Romulo Pinho, Benjamin Irving, Tarunashree Yavarna, Pim A. De Jong, Catalin Fetita, Margarete Ortner, Jan Sijbers

    Abstract:

    This paper describes a framework for establishing a reference Airway Tree segmentation, which was used to quantitatively evaluate 15 different Airway Tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented Airway Tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the Airway Tree. Finally, the reference Airway Trees are constructed by taking the union of all correctly extracted branch segments. Fifteen Airway Tree extraction algorithms from different research groups are evaluated on a diverse set of 20 chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in Airway segmentation algorithms.

  • Automatic segmentation of the Airway Tree from thoracic CT scans using a multi-threshold approach
    , 2009
    Co-Authors: Eva M. Van Rikxoort, Wouter Baggerman, Bram Van Ginneken

    Abstract:

    A method for automatic extraction of the Airway Tree from thoracic CT scans is presented that uses adaptive thresholds while grow- ing the Airways. The method is evaluated on 20 volumetric chest CT scans provided by the Extraction of Airways from CT 2009 (EXACT09) challenge. The scans were acquired at different sites, using several differ- ent scanners, scanning protocols, and reconstruction parameters. There are scans of clinical dose, low dose, and ultra-low dose data, in inspira- tion and expiration, from both relatively healthy and severely ill patients. The results show that the method is able to detect al arge number of Airway branches at the cost of relatively high leakage volume.