Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection

Rikxoort, Eva M. van; Hoop, Bartjan de; Viergever, Max A.; Prokop, Mathias; Ginneken, Bram van

doi:10.1118/1.3147146

Cited by 215 publications

(177 citation statements)

References 23 publications

(27 reference statements)

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“…The lung, fissure, and bronchial tree segmentations applied in this paper are all based on previous work ( [5], [6], [7]) and are therefore not described here. From the segmentation of the lungs, the lung borders are extracted as those voxels in the lung segmentation for which one of the 8-connected neighbors is outside the lung segmentation.…”

Section: Prerequisite Segmentationsmentioning

confidence: 99%

Automatic Segmentation of the Pulmonary Lobes from Fissures, Airways, and Lung Borders: Evaluation of Robustness against Missing Data

Rikxoort

Prokop

Hoop

et al. 2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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Abstract. Automatic segmentation of structures with missing or invisible borders is a challenging task. Since structures in the lungs are related, humans use contextual and shape information to infer the position of invisible borders. An example of a task in which the borders are often incomplete or invisible is the segmentation of the pulmonary lobes. In this paper, a fully automatic segmentation of the pulmonary lobes in chest CT scans is presented. The method is especially designed to be robust to incomplete fissures by incorporating contextual information from automatic lung, fissure, and bronchial tree segmentations, as well as shape information. Since the method relies on the result of automatic segmentations, it is important that the method is robust against failure of one or more of these segmentation methods. In an extensive experiment on 10 chest CT scans with manual segmentations, the robustness of the method to incomplete fissures and missing input segmentations is shown. In a second experiment on 100 chest CT scans with incomplete fissures, the method is shown to perform well.

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Section: Prerequisite Segmentationsmentioning

confidence: 99%

Automatic Segmentation of the Pulmonary Lobes from Fissures, Airways, and Lung Borders: Evaluation of Robustness against Missing Data

Rikxoort

Prokop

Hoop

et al. 2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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“…An observer with three years experience in chest CT checked the lung and airway segmentations for correctness. The lungs were extracted using an automatic segmentation algorithm [26]. The density of each voxel in the segmented lung volume was determined.…”

Section: Computed Tomographymentioning

confidence: 99%

Pulmonary function and CT biomarkers as risk factors for cardiovascular events in male lung cancer screening participants: the NELSON study

et al. 2014

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Objective The objective of this study was to investigate the association of spirometry and pulmonary CT biomarkers with cardiovascular events. Methods In this lung cancer screening trial 3,080 male participants without a prior cardiovascular event were analysed. Fatal and non-fatal cardiovascular events were included. Spirometry included forced expiratory volume measured in units of one-second percent predicted (FEV 1 %predicted) and FEV 1 divided by forced vital capacity (FVC; FEV 1 /FVC). CT examinations were quantified for coronary artery calcium volume, pulmonary emphysema (perc15) and bronchial wall thickness (pi10). Data were analysed via a Cox proportional hazard analysis, net reclassification improvement (NRI) and C-indices.Results 184 participants experienced a cardiovascular event during a median follow-up of 2.9 years. Age, pack-years and smoking status adjusted hazard ratios were 0.992 (95 % confidence interval (CI) 0.985-0.999) for FEV 1 %predicted, 1.000 (95%CI 0.986-1.015) for FEV 1 /FVC, 1.014 (95%CI 1.005-1.023) for perc15 per 10 HU, and 1.269 (95%CI 1.024-1.573) for pi10 per 1 mm. The incremental C-index (<0.015) and NRI (<2.8 %) were minimal. Coronary artery calcium volume had a hazard ratio of 1.046 (95%CI 1.034-1.058) per 100 mm 3 , an increase in C-index of 0.076 and an NRI of 16.9 % (P<0.0001). Conclusions Pulmonary CT biomarkers and spirometry measurements were significantly associated with cardiovascular events, but did not contain clinically relevant independent prognostic information for cardiovascular events. Key Points • Pulmonary CT biomarkers and spirometry are associated with cardiovascular events • These pulmonary measurements do not contain clinically relevant independent prognostic information • Only coronary calcium score improved cardiovascular risk prediction above age and smoking

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“…As a consequence, several groups have suggested algorithms specifically designed to handle CT images of pathological lungs by incorporating prior knowledge to guide the segmentation process. For example, Sluimer et al [4] and van Rikxoort et al [5] employed atlas-based techniques for the segmentation of lungs with arbitrary pathologic abnormalities. They were able to significantly increase segmentation accuracy, but the required non-linear atlas-to-image registration process was very time consuming.…”

Section: Introductionmentioning

confidence: 99%

A 4D Statistical Shape Model for Automated Segmentation of Lungs with Large Tumors

Wilms

Ehrhardt

Handels

2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract. Segmentation of lungs with large tumors is a challenging and time-consuming task, especially for 4D CT data sets used in radiation therapy. Existing lung segmentation methods are ineffective in these cases, because they are either not able to deal with large tumors and/or process every 3D image independently neglecting temporal information. In this paper, we present a approach for model-based 4D segmentation of lungs with large tumors in 4D CT data sets. In our approach, a 4D statistical shape model that accounts for inter-and intra-patient variability is fitted to the 4D image sequence, and the segmentation result is refined by a 4D graph-based optimal surface finding. The approach is evaluated using 10 4D CT data sets of lung tumor patients. The segmentation results are compared with a standard intensity-based approach and a 3D version of the presented model-based segmentation method. The intensity-based approach shows a better performance for normal lungs, however, fails in presence of large lung tumors. Although overall performance of 3D and 4D model-based segmentation is similar, the results indicate improved temporal coherence and improved robustness with respect to the segmentation parameters for the 4D model-based segmentation.

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Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection

Cited by 215 publications

References 23 publications

Automatic Segmentation of the Pulmonary Lobes from Fissures, Airways, and Lung Borders: Evaluation of Robustness against Missing Data

Automatic Segmentation of the Pulmonary Lobes from Fissures, Airways, and Lung Borders: Evaluation of Robustness against Missing Data

Pulmonary function and CT biomarkers as risk factors for cardiovascular events in male lung cancer screening participants: the NELSON study

A 4D Statistical Shape Model for Automated Segmentation of Lungs with Large Tumors

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