The Experts below are selected from a list of 2901 Experts worldwide ranked by ideXlab platform
Marleen De Bruijne - One of the best experts on this subject based on the ideXlab platform.
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An Airway Tree-shape Model for Geodesic Airway Branch Labeling
2011Co-Authors: Aasa Feragen, Joseph M. Reinhardt, Lo Pechin, Vladlena Gorbunova, Mads Nielsen, Asger Dirksen, François Lauze, Marleen De BruijneAbstract: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.
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vessel guided Airway Tree segmentation a voxel classification approach
Medical Image Analysis, 2010Co-Authors: Jon Sporring, Marleen De Bruijne, Haseem Ashraf, Jesper Holst PedersenAbstract: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.
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Vessel-guided Airway Tree segmentation: A voxel classification approach
Medical image analysis, 2010Co-Authors: Jon Sporring, Haseem Ashraf, Jesper Holst Pedersen, Marleen De BruijneAbstract: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
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Airway Tree extraction with locally optimal paths
Medical Image Computing and Computer-Assisted Intervention, 2009Co-Authors: Jon Sporring, Jesper Johannes Pedersen, Marleen De BruijneAbstract: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.
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Multiscale Vessel-guided Airway Tree Segmentation
2009Co-Authors: Jon Sporring, Marleen De BruijneAbstract: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. The method uses a voxel classification based appearance model, which involves the use of a classifier that is trained to differentiate between Airway and non-Airway voxels. Vessel and Airway orientation information are used in the form of a vessel orientation similarity measure, which indicates how similar the orientation of the an Airway candidate is to the orientation of the neighboring vessel. The method is evaluated within EXACT’09 on a diverse set of CT scans. Results show a favorable combination of a relatively large portion of the Tree detected correctly with very few false positives.
Jon Sporring - One of the best experts on this subject based on the ideXlab platform.
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vessel guided Airway Tree segmentation a voxel classification approach
Medical Image Analysis, 2010Co-Authors: Jon Sporring, Marleen De Bruijne, Haseem Ashraf, Jesper Holst PedersenAbstract: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.
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Vessel-guided Airway Tree segmentation: A voxel classification approach
Medical image analysis, 2010Co-Authors: Jon Sporring, Haseem Ashraf, Jesper Holst Pedersen, Marleen De BruijneAbstract: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
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Airway Tree extraction with locally optimal paths
Medical Image Computing and Computer-Assisted Intervention, 2009Co-Authors: Jon Sporring, Jesper Johannes Pedersen, Marleen De BruijneAbstract: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.
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Multiscale Vessel-guided Airway Tree Segmentation
2009Co-Authors: Jon Sporring, Marleen De BruijneAbstract: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. The method uses a voxel classification based appearance model, which involves the use of a classifier that is trained to differentiate between Airway and non-Airway voxels. Vessel and Airway orientation information are used in the form of a vessel orientation similarity measure, which indicates how similar the orientation of the an Airway candidate is to the orientation of the neighboring vessel. The method is evaluated within EXACT’09 on a diverse set of CT scans. Results show a favorable combination of a relatively large portion of the Tree detected correctly with very few false positives.
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MICCAI (1) - Airway Tree Extraction with Locally Optimal Paths
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2009Co-Authors: Jon Sporring, Jesper Johannes Pedersen, Marleen De BruijneAbstract: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.
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Extraction of Airways from CT (EXACT'09).
IEEE Transactions on Medical Imaging, 2012Co-Authors: Bram Van Ginneken, Joseph M. Reinhardt, Romulo Pinho, Jan Sijbers, Benjamin Irving, Tarunashree Yavarna, Pim A. De Jong, Catalin Fetita, Margarete Ortner, Marco FeuersteinAbstract: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.
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Extraction of Airways From CT (EXACT'09)
IEEE transactions on medical imaging, 2012Co-Authors: Bram Van Ginneken, Joseph M. Reinhardt, Romulo Pinho, Benjamin Irving, Tarunashree Yavarna, Pim A. De Jong, Catalin Fetita, Margarete Ortner, Jan SijbersAbstract: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.
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Automatic segmentation of the Airway Tree from thoracic CT scans using a multi-threshold approach
2009Co-Authors: Eva M. Van Rikxoort, Wouter Baggerman, Bram Van GinnekenAbstract: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.
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robust segmentation and anatomical labeling of the Airway Tree from thoracic ct scans
Medical Image Computing and Computer-Assisted Intervention, 2008Co-Authors: Bram Van Ginneken, Wouter Baggerman, Eva M RikxoortAbstract:A method for automatic extraction and labeling of the Airway Tree from thoracic CT scans is presented and extensively evaluated on 150 scans of clinical dose, low dose and ultra-low dose data, in inspiration and expiration from both relatively healthy and severely ill patients. The method uses adaptive thresholds while growing the Airways and it is shown that this strategy leads to a substantial increase in the number, total length and number of correctly labeled Airways extracted. From inspiration scans on average 170 branches are found, from expiration scans 59.
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MICCAI (1) - Robust Segmentation and Anatomical Labeling of the Airway Tree from Thoracic CT Scans
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2008Co-Authors: Bram Van Ginneken, Wouter Baggerman, Eva M. Van RikxoortAbstract:A method for automatic extraction and labeling of the Airway Tree from thoracic CT scans is presented and extensively evaluated on 150 scans of clinical dose, low dose and ultra-low dose data, in inspiration and expiration from both relatively healthy and severely ill patients. The method uses adaptive thresholds while growing the Airways and it is shown that this strategy leads to a substantial increase in the number, total length and number of correctly labeled Airways extracted. From inspiration scans on average 170 branches are found, from expiration scans 59.
Milan Sonka - One of the best experts on this subject based on the ideXlab platform.
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optimal graph search based segmentation of Airway Tree double surfaces across bifurcations
IEEE Transactions on Medical Imaging, 2013Co-Authors: Xiaomin Liu, Eric A. Hoffman, Danny Z. Chen, Merryn H. Tawhai, Milan SonkaAbstract:Identification of both the luminal and the wall areas of the bronchial Tree structure from volumetric X-ray computed tomography (CT) data sets is of critical importance in distinguishing important phenotypes within numerous major lung diseases including chronic obstructive pulmonary diseases (COPD) and asthma. However, accurate assessment of the inner and outer Airway wall surfaces of a complete 3-D Tree structure is difficult due to their complex nature, particularly around the branch areas. In this paper, we extend a graph search based technique (LOGISMOS) to simultaneously identify multiple inter-related surfaces of branching Airway Trees. We first perform a presegmentation of the input 3-D image to obtain basic information about the Tree topology. The presegmented image is resampled along judiciously determined paths to produce a set of vectors of voxels (called voxel columns). The resampling process utilizes medial axes to ensure that voxel columns of appropriate lengths and directions are used to capture the object surfaces without interference. A geometric graph is constructed whose edges connect voxels in the resampled voxel columns and enforce validity of the smoothness and separation constraints on the sought surfaces. Cost functions with directional information are employed to distinguish inner and outer walls. The assessment of wall thickness measurement on a CT-scanned double-wall physical phantom (patterned after an in vivo imaged human Airway Tree) achieved highly accurate results on the entire 3-D Tree. The observed mean signed error of wall thickness ranged from -0.09 ±0.24 mm to 0.07 ±0.23 mm in bifurcating/nonbifurcating areas. The mean unsigned errors were 0.16±0.12 mm to 0.20±0.11 mm. When the Airway wall surface was partitioned into meaningful subregions, the Airway wall thickness accuracy was the same in most tested bifurcation/nonbifurcation and carina/noncarina regions (p=NS). Once validated on phantoms, our method was applied to human in vivo volumetric CT data to demonstrate relationships of Airway wall thickness as a function of luminal dimension and Airway Tree generation. Wall thickness differences between the bifurcation/nonbifurcation regions were statistically significant (p <; 0.05) for Tree generations 6, 7, 8, and 9. In carina/noncarina regions, the wall thickness was statistically different in generations 1, 4, 5, 6, 7, and 8.
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Optimal Graph Search Based Segmentation of Airway Tree Double Surfaces Across Bifurcations
IEEE transactions on medical imaging, 2012Co-Authors: Xiaomin Liu, Eric A. Hoffman, Danny Z. Chen, Merryn H. Tawhai, Milan SonkaAbstract:Identification of both the luminal and the wall areas of the bronchial Tree structure from volumetric X-ray computed tomography (CT) data sets is of critical importance in distinguishing important phenotypes within numerous major lung diseases including chronic obstructive pulmonary diseases (COPD) and asthma. However, accurate assessment of the inner and outer Airway wall surfaces of a complete 3-D Tree structure is difficult due to their complex nature, particularly around the branch areas. In this paper, we extend a graph search based technique (LOGISMOS) to simultaneously identify multiple inter-related surfaces of branching Airway Trees. We first perform a presegmentation of the input 3-D image to obtain basic information about the Tree topology. The presegmented image is resampled along judiciously determined paths to produce a set of vectors of voxels (called voxel columns). The resampling process utilizes medial axes to ensure that voxel columns of appropriate lengths and directions are used to capture the object surfaces without interference. A geometric graph is constructed whose edges connect voxels in the resampled voxel columns and enforce validity of the smoothness and separation constraints on the sought surfaces. Cost functions with directional information are employed to distinguish inner and outer walls. The assessment of wall thickness measurement on a CT-scanned double-wall physical phantom (patterned after an in vivo imaged human Airway Tree) achieved highly accurate results on the entire 3-D Tree. The observed mean signed error of wall thickness ranged from -0.09 ±0.24 mm to 0.07 ±0.23 mm in bifurcating/nonbifurcating areas. The mean unsigned errors were 0.16±0.12 mm to 0.20±0.11 mm. When the Airway wall surface was partitioned into meaningful subregions, the Airway wall thickness accuracy was the same in most tested bifurcation/nonbifurcation and carina/noncarina regions (p=NS). Once validated on phantoms, our method was applied to human in vivo volumetric CT data to demonstrate relationships of Airway wall thickness as a function of luminal dimension and Airway Tree generation. Wall thickness differences between the bifurcation/nonbifurcation regions were statistically significant (p
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Quantitative analysis of pulmonary Airway Tree structures.
Computers in biology and medicine, 2005Co-Authors: Kálmán Palágyi, Eric A. Hoffman, Juerg Tschirren, Milan SonkaAbstract:A method for computationally efficient skeletonization of three-dimensional tubular structures is reported. The method is specifically targeting skeletonization of vascular and Airway Tree structures in medical images but it is general and applicable to many other skeletonization tasks. The developed approach builds on the following novel concepts and properties: fast curve-thinning algorithm to increase computational speed, endpoint re-checking to avoid generation of spurious side branches, depth-and-length sensitive pruning, and exact Tree-branch partitioning allowing branch volume and surface measurements. The method was validated in computer and physical phantoms and in vivo CT scans of human lungs. The validation studies demonstrated sub-voxel accuracy of branch point positioning, insensitivity to changes of object orientation, and high reproducibility of derived quantitative indices of the tubular structures offering a significant improvement over previously reported methods (p
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Matching and anatomical labeling of human Airway Tree
IEEE transactions on medical imaging, 2005Co-Authors: Juerg Tschirren, Eric A. Hoffman, G. Mclennan, Kálmán Palágyi, Milan SonkaAbstract:Matching of corresponding branchpoints between two human Airway Trees, as well as assigning anatomical names to the segments and branchpoints of the human Airway Tree, are of significant interest for clinical applications and physiological studies. In the past, these tasks were often performed manually due to the lack of automated algorithms that can tolerate false branches and anatomical variability typical for in vivo Trees. In this paper, we present algorithms that perform both matching of branchpoints and anatomical labeling of in vivo Trees without any human intervention and within a short computing time. No hand-pruning of false branches is required. The results from the automated methods show a high degree of accuracy when validated against reference data provided by human experts. 92.9% of the verifiable branchpoint matches found by the computer agree with experts' results. For anatomical labeling, 97.1% of the automatically assigned segment labels were found to be correct.
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Airway Tree segmentation using adaptive regions of interest
Medical Imaging 2004: Physiology Function and Structure from Medical Images, 2004Co-Authors: Juerg Tschirren, Geoffrey Mclennan, Eric A. Hoffman, Milan SonkaAbstract:The accurate segmentation of the human Airway Tree from volumetric CT images builds an important corner stone in pulmonary image processing. It is the basis for many consecutive processing steps like branch-point labeling and matching, virtual bronchoscopy, and more. Previously reported Airway Tree segmentation methods often suffer from "leaking" into the surrounding lung tissue, caused by the anatomically thin Airway wall combined with the occurrence of partial volume effect and noise. Another common problem with previously proposed Airway segmentation algorithms is their difficulties with segmenting low dose scans and scans of heavily diseased lungs. We present a new Airway Tree segmentation method that works in 3D, avoids leaks, and automatically adapts to different types of scans without the need for the user to iteratively adjust any parameters.
Eric A. Hoffman - One of the best experts on this subject based on the ideXlab platform.
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ACCV Workshops (3) - A Novel Iterative Method for Airway Tree Segmentation from CT Imaging Using Multiscale Leakage Detection
Computer Vision – ACCV 2016 Workshops, 2017Co-Authors: Syed Ahmed Nadeem, Eric A. Hoffman, Dakai Jin, Punam K SahaAbstract:Computed tomography (CT)-based metrics of Airway phenotypes, wall-thickness, and other morphological features are increasingly being used in large multi-center lung studies involving many hundreds or thousands of image datasets. There is an unmet need for a fully reliable, automated algorithm for CT-based segmentation of Airways. State-of-the-art methods require a post-editing step, which is time consuming when several thousands of image data sets need to be reviewed and edited. In this paper, we present a novel iterative algorithm for CT-based segmentation of Airway Trees. Early testing suggests that the method requires no editing to extract a set of Airway segments along a standardized set of bronchial paths extending two generations beyond the segmental Airways. It uses simple intensity-based connectivity and new leakage detection and volume freezing algorithms to iteratively grow an Airway Tree. It starts with an initial, automatically determined seed inside the trachea and a conservative threshold; applies region growing and generates a leakage-corrected segmentation; freezes the segmented volume; and shifts the threshold toward a more generous value for the next iteration until a convergence occurs. The method was applied on chest CT scans of fifteen normal non-smoking subjects. Airway segmentation results were compared with manually edited results, and branch level accuracy of the new segmentation method was examined along five standardized segmental Airway paths and continuing to two generations beyond the segmental paths. The method successfully detected all branches up to two generations beyond the five segmental Airway paths with no visual leakages.
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a novel iterative method for Airway Tree segmentation from ct imaging using multiscale leakage detection
Asian Conference on Computer Vision, 2016Co-Authors: Syed Ahmed Nadeem, Eric A. Hoffman, Dakai Jin, Punam K SahaAbstract:Computed tomography (CT)-based metrics of Airway phenotypes, wall-thickness, and other morphological features are increasingly being used in large multi-center lung studies involving many hundreds or thousands of image datasets. There is an unmet need for a fully reliable, automated algorithm for CT-based segmentation of Airways. State-of-the-art methods require a post-editing step, which is time consuming when several thousands of image data sets need to be reviewed and edited. In this paper, we present a novel iterative algorithm for CT-based segmentation of Airway Trees. Early testing suggests that the method requires no editing to extract a set of Airway segments along a standardized set of bronchial paths extending two generations beyond the segmental Airways. It uses simple intensity-based connectivity and new leakage detection and volume freezing algorithms to iteratively grow an Airway Tree. It starts with an initial, automatically determined seed inside the trachea and a conservative threshold; applies region growing and generates a leakage-corrected segmentation; freezes the segmented volume; and shifts the threshold toward a more generous value for the next iteration until a convergence occurs. The method was applied on chest CT scans of fifteen normal non-smoking subjects. Airway segmentation results were compared with manually edited results, and branch level accuracy of the new segmentation method was examined along five standardized segmental Airway paths and continuing to two generations beyond the segmental paths. The method successfully detected all branches up to two generations beyond the five segmental Airway paths with no visual leakages.
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optimal graph search based segmentation of Airway Tree double surfaces across bifurcations
IEEE Transactions on Medical Imaging, 2013Co-Authors: Xiaomin Liu, Eric A. Hoffman, Danny Z. Chen, Merryn H. Tawhai, Milan SonkaAbstract:Identification of both the luminal and the wall areas of the bronchial Tree structure from volumetric X-ray computed tomography (CT) data sets is of critical importance in distinguishing important phenotypes within numerous major lung diseases including chronic obstructive pulmonary diseases (COPD) and asthma. However, accurate assessment of the inner and outer Airway wall surfaces of a complete 3-D Tree structure is difficult due to their complex nature, particularly around the branch areas. In this paper, we extend a graph search based technique (LOGISMOS) to simultaneously identify multiple inter-related surfaces of branching Airway Trees. We first perform a presegmentation of the input 3-D image to obtain basic information about the Tree topology. The presegmented image is resampled along judiciously determined paths to produce a set of vectors of voxels (called voxel columns). The resampling process utilizes medial axes to ensure that voxel columns of appropriate lengths and directions are used to capture the object surfaces without interference. A geometric graph is constructed whose edges connect voxels in the resampled voxel columns and enforce validity of the smoothness and separation constraints on the sought surfaces. Cost functions with directional information are employed to distinguish inner and outer walls. The assessment of wall thickness measurement on a CT-scanned double-wall physical phantom (patterned after an in vivo imaged human Airway Tree) achieved highly accurate results on the entire 3-D Tree. The observed mean signed error of wall thickness ranged from -0.09 ±0.24 mm to 0.07 ±0.23 mm in bifurcating/nonbifurcating areas. The mean unsigned errors were 0.16±0.12 mm to 0.20±0.11 mm. When the Airway wall surface was partitioned into meaningful subregions, the Airway wall thickness accuracy was the same in most tested bifurcation/nonbifurcation and carina/noncarina regions (p=NS). Once validated on phantoms, our method was applied to human in vivo volumetric CT data to demonstrate relationships of Airway wall thickness as a function of luminal dimension and Airway Tree generation. Wall thickness differences between the bifurcation/nonbifurcation regions were statistically significant (p <; 0.05) for Tree generations 6, 7, 8, and 9. In carina/noncarina regions, the wall thickness was statistically different in generations 1, 4, 5, 6, 7, and 8.
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Optimal Graph Search Based Segmentation of Airway Tree Double Surfaces Across Bifurcations
IEEE transactions on medical imaging, 2012Co-Authors: Xiaomin Liu, Eric A. Hoffman, Danny Z. Chen, Merryn H. Tawhai, Milan SonkaAbstract:Identification of both the luminal and the wall areas of the bronchial Tree structure from volumetric X-ray computed tomography (CT) data sets is of critical importance in distinguishing important phenotypes within numerous major lung diseases including chronic obstructive pulmonary diseases (COPD) and asthma. However, accurate assessment of the inner and outer Airway wall surfaces of a complete 3-D Tree structure is difficult due to their complex nature, particularly around the branch areas. In this paper, we extend a graph search based technique (LOGISMOS) to simultaneously identify multiple inter-related surfaces of branching Airway Trees. We first perform a presegmentation of the input 3-D image to obtain basic information about the Tree topology. The presegmented image is resampled along judiciously determined paths to produce a set of vectors of voxels (called voxel columns). The resampling process utilizes medial axes to ensure that voxel columns of appropriate lengths and directions are used to capture the object surfaces without interference. A geometric graph is constructed whose edges connect voxels in the resampled voxel columns and enforce validity of the smoothness and separation constraints on the sought surfaces. Cost functions with directional information are employed to distinguish inner and outer walls. The assessment of wall thickness measurement on a CT-scanned double-wall physical phantom (patterned after an in vivo imaged human Airway Tree) achieved highly accurate results on the entire 3-D Tree. The observed mean signed error of wall thickness ranged from -0.09 ±0.24 mm to 0.07 ±0.23 mm in bifurcating/nonbifurcating areas. The mean unsigned errors were 0.16±0.12 mm to 0.20±0.11 mm. When the Airway wall surface was partitioned into meaningful subregions, the Airway wall thickness accuracy was the same in most tested bifurcation/nonbifurcation and carina/noncarina regions (p=NS). Once validated on phantoms, our method was applied to human in vivo volumetric CT data to demonstrate relationships of Airway wall thickness as a function of luminal dimension and Airway Tree generation. Wall thickness differences between the bifurcation/nonbifurcation regions were statistically significant (p
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Human Airway Tree structure query atlas
Proceedings of SPIE, 2010Co-Authors: Gary E. Christensen, Nathan Burnette, Weichen Gao, Matineh Shaker, Joseph M. Reinhardt, Janice Cook-granroth, Geoffrey Mclennan, Eric A. HoffmanAbstract:A queryable electronic atlas was developed to quantitatively characterize the normal human lung Airway Tree and to provide a better understanding of the lung for diagnosing diseases and evaluating treatments. The atlas consists of Airway measurements taken from CT images using the Pulmonary Workstation II (PW2) software package. These measurements include Airway cross-sectional area at midpoint between branch points; maximum and minimum diameter of a particular Airway cross section at segment midpoint; average, maximum, and minimum wall thickness per branch; and wall thickness uniformity within a branch. The atlas provides user friendly interfaces for interrogating population statistics, comparing populations, comparing individuals to populations, and comparing individuals to other individuals. Populations can be selected based on age, gender, race, ethnicity, and normalcy/disease.
