Automatic airway-artery analysis on lung CT to quantify airway wall thickening and bronchiectasis.

PURPOSE Bronchiectasis and airway wall thickening are commonly assessed in computed tomography (CT) by comparing the airway size with the size of the accompanying artery. Thus, in order to automate the quantification of bronchiectasis and wall thickening following a similar principle, there is a need for methods that automatically segment the airway and vascular trees, measure their size, and pair each airway branch with its accompanying artery. METHODS This paper combines and extends existing techniques to present a fully automated pipeline that, given a thoracic chest CT, segments, measures, and pairs airway branches with the accompanying artery, then quantifies airway wall thickening and bronchiectasis by measuring the wall-artery ratio (WAR) and lumen and outer wall airway-artery ratio (AAR). Measurements that do not use the artery size for normalization are also extracted, including wall area percentage (WAP), wall thickness ratio (WTR), and airway diameters. RESULTS The method was thoroughly evaluated using 8000 manual annotations of airway-artery pairs from 24 full-inspiration pediatric CT scans (12 diseased and 12 controls). Limits of agreement between the automatically and manually measured diameters were comparable to interobserver limits of agreement. Differences in automatically obtained WAR, AAR, WAP, and WTR between bronchiectatic subjects and controls were similar as when manual annotations were used: WAR and outer AAR were significantly higher in the bronchiectatic subjects (p < 0.05), but lumen AAR, WAP, and WTR were not. Only measurements that use artery size for normalization led to significant differences between groups, highlighting the importance of airway-artery pairing. CONCLUSIONS The fully automatic method presented in this paper could replace time-consuming manual annotations and visual scoring methods to quantify abnormal widening and thickening of airways.

[1]  Heinz-Otto Peitgen,et al.  Reproducibility of airway wall thickness measurements , 2010, Medical Imaging.

[2]  Stefan Klein,et al.  Cerebellum segmentation in MRI using atlas registration and local multi-scale image descriptors , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[3]  David P. Naidich,et al.  An evaluation of automated broncho-arterial ratios for reliable assessment of bronchiectasis , 2008, SPIE Medical Imaging.

[4]  Brian B. Avants,et al.  Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge , 2011, IEEE Transactions on Medical Imaging.

[5]  Marleen de Bruijne,et al.  Shape Particle Filtering for Image Segmentation , 2004, MICCAI.

[6]  Marleen de Bruijne,et al.  Integrating local voxel classification and global shape models for medical image segmentation , 2008, Medical Imaging: Image Processing.

[7]  Christine H. Lorenz,et al.  Second International Workshop on Pulmonary Image Analysis , 2009 .

[8]  Majid Mirmehdi,et al.  MICCAI Workshop on Probabilistic Models for Medical Image Analysis , 2009 .

[9]  Catalin I. Fetita,et al.  3D mapping of airway wall thickening in asthma with MSCT: a level set approach , 2014, Medical Imaging.

[10]  Marleen de Bruijne,et al.  MACD: an imaging marker for cardiovascular disease , 2010, Medical Imaging.

[11]  I. Masters,et al.  Bronchoarterial ratio on high-resolution CT scan of the chest in children without pulmonary pathology: need to redefine bronchial dilatation. , 2010, Chest.

[12]  Marleen de Bruijne,et al.  Bicycle chain shape models , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[13]  W. Hop,et al.  Changes in airway dimensions on computed tomography scans of children with cystic fibrosis. , 2005, American journal of respiratory and critical care medicine.

[14]  Stefan Klein,et al.  Conditional Shape Models for Cardiac Motion Estimation , 2010, MICCAI.

[15]  Marleen de Bruijne,et al.  Geodesic Atlas-Based Labeling of Anatomical Trees: Application and Evaluation on Airways Extracted From CT , 2015, IEEE Transactions on Medical Imaging.

[16]  Marleen de Bruijne,et al.  Vessel tree extraction using locally optimal paths , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[17]  Cristian Lorenz,et al.  Simultaneous segmentation and tree reconstruction of the airways for virtual bronchoscopy , 2002, SPIE Medical Imaging.

[18]  F. Laurent,et al.  Assessment of bronchial wall thickness and lumen diameter in human adults using multi‐detector computed tomography: comparison with theoretical models , 2007, Journal of anatomy.

[19]  Marleen de Bruijne,et al.  Vessel-guided airway tree segmentation: A voxel classification approach , 2010, Medical Image Anal..

[20]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[21]  D. McCauley,et al.  Cystic fibrosis: scoring system with thin-section CT. , 1991, Radiology.

[22]  P. Molina,et al.  High-resolution computed tomography in young patients with cystic fibrosis: distribution of abnormalities and correlation with pulmonary function tests. , 2004, The Journal of pediatrics.

[23]  J. Mayo,et al.  Progressive damage on high resolution computed tomography despite stable lung function in cystic fibrosis , 2004, European Respiratory Journal.

[24]  Marleen de Bruijne,et al.  Adapting Active Shape Models for 3D Segmentation of Tubular Structures in Medical Images , 2003, IPMI.

[25]  Peter D Sly,et al.  Assessment of early bronchiectasis in young children with cystic fibrosis is dependent on lung volume. , 2013, Chest.

[26]  R. Castile,et al.  Model of forced expiratory flows and airway geometry in infants. , 2004, Journal of applied physiology.

[27]  Gabor Fichtinger,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008, 11th International Conference, New York, NY, USA, September 6-10, 2008, Proceedings, Part I , 2008, International Conference on Medical Image Computing and Computer-Assisted Intervention.

[28]  Atsushi Imiya,et al.  Structural, Syntactic, and Statistical Pattern Recognition , 2012, Lecture Notes in Computer Science.

[29]  Marleen de Bruijne,et al.  2D-3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models , 2011, Medical Image Anal..

[30]  Marleen de Bruijne,et al.  First International Workshop on Pulmonary Image Analysis , 2008 .

[31]  Stefan Klein,et al.  Early diagnosis of dementia based on intersubject whole-brain dissimilarities , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[32]  Marleen de Bruijne,et al.  Weight Preserving Image Registration for Monitoring Disease Progression in Lung CT , 2008, MICCAI.

[33]  Dinggang Shen,et al.  Multimodal Brain Image Analysis: First International Workshop, MBIA 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011, ... Vision, Pattern Recognition, and Graphics) , 2011 .

[34]  R. Castile,et al.  Structural airway abnormalities in infants and young children with cystic fibrosis. , 2004, The Journal of pediatrics.

[35]  Max A. Viergever,et al.  Automated Segmentation of Abdominal Aortic Aneurysms in Multi-spectral MR Images , 2003, MICCAI.

[36]  W. Hop,et al.  Pulmonary disease assessment in cystic fibrosis: comparison of CT scoring systems and value of bronchial and arterial dimension measurements. , 2004, Radiology.

[37]  Vladimir Kolmogorov,et al.  An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Petia Radeva,et al.  Lung tissue classification in severe advanced cystic fibrosis from CT scans , 2011 .

[39]  Marleen de Bruijne,et al.  A Pattern Classification Approach to Aorta Calcium Scoring in Radiographs , 2005, CVBIA.

[40]  P. D. de Jong,et al.  A CT scan score for the assessment of lung disease in children with common variable immunodeficiency disorders. , 2010, Chest.

[41]  Hiroshi Murase,et al.  Automated Nomenclature of Bronchial Branches Extracted from CT Images and Its Application to Biopsy Path Planning in Virtual Bronchoscopy , 2005, MICCAI.

[42]  Marleen de Bruijne,et al.  Cystic fibrosis: are volumetric ultra-low-dose expiratory CT scans sufficient for monitoring related lung disease? , 2009, Radiology.

[43]  H. Tiddens,et al.  Tracking CF disease progression with CT and respiratory symptoms in a cohort of children aged 6–19 years , 2014, Pediatric pulmonology.

[44]  Lauge Sørensen,et al.  Multiple Classifier Systems in Texton-Based Approach for the Classification of CT Images of Lung , 2010, MCV.

[45]  Marleen de Bruijne,et al.  An Airway Tree-shape Model for Geodesic Airway Branch Labeling , 2011 .

[46]  James S. Duncan,et al.  Medical Image Analysis , 1999, IEEE Pulse.

[47]  Lauge Sørensen,et al.  Multi-object tracking of human spermatozoa , 2008, SPIE Medical Imaging.

[48]  Catalin I. Fetita,et al.  Grading remodeling severity in asthma based on airway wall thickening index and bronchoarterial ratio measured with MSCT , 2015, Medical Imaging.

[49]  Marleen de Bruijne,et al.  Robust Shape Regression for Supervised Vessel Segmentation and its Application to Coronary Segmentation in CTA , 2011, IEEE Transactions on Medical Imaging.

[50]  P. Sly,et al.  Lung disease at diagnosis in infants with cystic fibrosis detected by newborn screening. , 2009, American journal of respiratory and critical care medicine.

[51]  S. van Noorden,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010, 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part II , 2010, MICCAI.

[52]  P. Boelle,et al.  HRCT and MRI of the lung in children with cystic fibrosis: comparison of different scoring systems. , 2014, Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society.

[53]  Fereidoun Abtin,et al.  A Bottom-up approach for labeling of human airway trees , 2011 .

[54]  Ian Lewis,et al.  Proceedings of the SPIE , 2012 .

[55]  Marleen de Bruijne,et al.  Obstructive pulmonary function: Patient classification using 3D registration of inspiration and expiration CT images , 2009 .

[56]  E.E. Pissaloux,et al.  Image Processing , 1994, Proceedings. Second Euromicro Workshop on Parallel and Distributed Processing.

[57]  Mads Nielsen,et al.  A pixelwise inpainting-based refinement scheme for quantizing calcification in the lumbar aorta on 2D lateral x-ray images , 2006, SPIE Medical Imaging.

[58]  Marleen de Bruijne,et al.  Airway Tree Extraction with Locally Optimal Paths , 2009, MICCAI.

[59]  Hans-Ulrich Kauczor,et al.  Automatic Airway Analysis on Multidetector Computed Tomography in Cystic Fibrosis: Correlation With Pulmonary Function Testing , 2013, Journal of thoracic imaging.

[60]  Raúl San José Estépar,et al.  Association between airway caliber changes with lung inflation and emphysema assessed by volumetric CT scan in subjects with COPD. , 2012, Chest.

[61]  Marleen de Bruijne,et al.  Multi-object Segmentation Using Shape Particles , 2005, IPMI.

[62]  J. Smeets,et al.  Mass Is All That Matters in the Size–Weight Illusion , 2012, PloS one.

[63]  Nobuhiko Hata,et al.  Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 , 2014, Lecture Notes in Computer Science.

[64]  Stefan Klein,et al.  Plaque characterization in ex vivo MRI evaluated by dense 3D correspondence with histology , 2011, Medical Imaging.

[65]  H. Duivenvoorden,et al.  Impact of bronchiectasis and trapped air on quality of life and exacerbations in cystic fibrosis , 2013, European Respiratory Journal.

[66]  Marleen de Bruijne,et al.  Supervised shape analysis for risk assessment in osteoporosis , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[67]  Vladimir Kolmogorov,et al.  An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision , 2004, IEEE Trans. Pattern Anal. Mach. Intell..

[68]  Marleen de Bruijne,et al.  Prior knowledge regularization in statistical medical image tasks , 2009 .

[69]  Marleen de Bruijne,et al.  Confidence of model based shape reconstruction from sparse data , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[70]  Marleen de Bruijne,et al.  Voxel classification based airway tree segmentation , 2008, SPIE Medical Imaging.

[71]  J. F. Lerallut,et al.  Automated airway evaluation system for multi-slice computed tomography using airway lumen diameter, airway wall thickness and broncho-arterial ratio , 2006, SPIE Medical Imaging.

[72]  Simon Ameer-Beg,et al.  Biomedical Imaging: From Nano to Macro , 2008 .

[73]  Mads Nielsen,et al.  Quantifying Calcification in the Lumbar Aorta on X-Ray Images , 2007, MICCAI.

[74]  Lauge Sørensen,et al.  Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns , 2010, IEEE Transactions on Medical Imaging.

[75]  Eric A. Hoffman,et al.  Extraction of Airways From CT (EXACT'09) , 2012, IEEE Transactions on Medical Imaging.

[76]  J E Aldrich,et al.  Chronic infiltrative lung disease: comparison of diagnostic accuracies of radiography and low- and conventional-dose thin-section CT. , 1994, Radiology.

[77]  F. Deconinck,et al.  Information Processing in Medical Imaging , 1984, Springer Netherlands.

[78]  A. Bankier,et al.  Chronic obstructive pulmonary disease: CT quantification of airways disease. , 2012, Radiology.

[79]  Marleen de Bruijne,et al.  Toward automated detection and segmentation of aortic calcifications from radiographs , 2007, SPIE Medical Imaging.

[80]  W. Dorland,et al.  Plasma Physics and Controlled Fusion , 1984 .

[81]  Marleen de Bruijne,et al.  Improved Tissue Segmentation by Including an MR Acquisition Model , 2011, MBIA.

[82]  S. Stanojevic,et al.  Multi-ethnic reference values for spirometry for the 3–95-yr age range: the global lung function 2012 equations , 2012, European Respiratory Journal.

[83]  Metin Nafi Gürcan,et al.  Image Analysis for Cystic Fibrosis: Computer-Assisted Airway Wall and Vessel Measurements from Low-Dose, Limited Scan Lung CT Images , 2013, Journal of Digital Imaging.

[84]  Marleen de Bruijne,et al.  Optimal Graph Based Segmentation Using Flow Lines with Application to Airway Wall Segmentation , 2011, IPMI.

[85]  Sotirios A. Tsaftaris,et al.  Medical Image Computing and Computer Assisted Intervention , 2017 .

[86]  D. McCauley,et al.  Bronchiectasis: CT evaluation. , 1993, AJR. American journal of roentgenology.

[87]  H. Gietema,et al.  Visual versus Automated Evaluation of Chest Computed Tomography for the Presence of Chronic Obstructive Pulmonary Disease , 2012, PloS one.

[88]  Max A. Viergever,et al.  Active-shape-model-based segmentation of abdominal aortic aneurysms in CTA images , 2002, SPIE Medical Imaging.

[89]  M. de Bruijne,et al.  A computer-based measure of irregularity in vertebral alignment is a BMD-independent predictor of fracture risk in postmenopausal women , 2007, Osteoporosis International.

[90]  Yasutaka Nakano,et al.  The prediction of small airway dimensions using computed tomography. , 2005, American journal of respiratory and critical care medicine.

[91]  Marleen de Bruijne,et al.  Mass preserving registration for lung CT , 2009, Medical Imaging.

[92]  Max A. Viergever,et al.  Model-based segmentation of abdominal aortic aneurysms in CTA images , 2003, SPIE Medical Imaging.

[93]  J. Lammers,et al.  CT Screening for Pulmonary Pathology in Common Variable Immunodeficiency Disorders and the Correlation with Clinical and Immunological Parameters , 2014, Journal of Clinical Immunology.

[94]  Reinhard Klette,et al.  Computer Vision - ACCV 2010 - 10th Asian Conference on Computer Vision, Queenstown, New Zealand, November 8-12, 2010, Revised Selected Papers, Part III , 2011, ACCV.

[95]  Marleen de Bruijne,et al.  Optimal surface segmentation using flow lines to quantify airway abnormalities in chronic obstructive pulmonary disease , 2014, Medical Image Anal..