Jelenlegi hely

Quantitative analysis of tubular tree structures

Lifetime from: 
2002
Lifetime to: 
2006
Short description: 
A method for computationally efficient skeletonization of three-dimensional tubular structures was proposeded. It is specifically targeting skeletonization of vascular and airway tree structures in medical images but it is general and applicable to many other skeletonization tasks.
Description: 

Description

Tubular structures are frequently found in living organisms. The tubes — e.g., arteries or veins — are organized into more complex structures. Trees consisting of tubular segments form the arterial and venous systems, intrathoracic airways form bronchial trees, and other examples can be found.

Computed tomography (CT) or magnetic resonance (MR) imaging provides volumetric image data allowing identification of such tree structures. Frequently, the trees represented as contiguous sets of voxels must be quantitatively analyzed. The analysis may be substantially simplified if the voxel-level tree is represented in a formal tree structure consisting of a set of nodes and connecting arcs. To build such formal trees, the voxel-level tree object must be transformed into a set of interconnected single-voxel centerlines representing individual tree branches. Therefore, the aim of our work was to develop a robust method for identification of centerlines and bifurcation (trifurcation, etc.) points in segmented tubular tree structures acquired in vivo from humans and animals using volumetric CT or MR scanning, rotational angiography, or other volumetric imaging means.

Our method allows to quantitatively analyze tubular tree structures. Assuming that an imperfectly segmented tree was obtained from volumetric data in the previous stages, the presented technique allows to obtain a single-voxel skeleton of the tree while overcoming many segmentation imperfections, yields formal tree representation, and performs quantitative analysis of individual tree segments on a tree-branch basis. The input of the proposed method is a 3D binary image representing a segmented voxel-level tree object. All main components of our method were specifically developed to deal with imaging artifacts typically present in volumetric medical image data. As such, the method consists of the following main steps: (1) airway segmentation, (2) correction of the segmented tree, (3) identification of the tree root, (4) extraction of the 3D centerline—skeletonization, (5) tree pruning, (6) centerline smoothing, (7) identification of branch-points, (8) generation of a formal tree structure, (9) tree partitioning, and (10) quantitative analysis.

The key steps are now illustrated:


Segmentation - adaptive region growing (left), extracting centerlines - topologically and geometrically correct thinning (middle), and excluding branch-areas based on 3D distance map calculation (right).


Partitioning centerlines in a formal tree data structure (left), partitioning segmented tree via isotropic label propagation (middle), and quantitative analysis formal XML tree with associated measurements (right).

Publications: 
Quantitative analysis of pulmonary airway tree structures, Palágyi, Kálmán, Tschirren Juerg, Hoffman Eric A., and Sonka Milan , COMPUTERS IN BIOLOGY AND MEDICINE, 2006///, Volume 36, Issue 9, ANTIGA L, 2003, IEEE T MED IMAGING, V22, P674, DOI10.1109/TMI.2003.812261 AYLWARD SR, 2002, IEEE T MED IMAGING, V21, P61 BLAND JM, 1986, LANCET, V1, P307 BORGEFORS G, 1984, COMPUT VISION GRAPH, V27, P321 BOUIX S, 2003, IEEE C COMP VIS PATT, P449 CHEN ZK, , p.974 - 996, (2006)
Matching and anatomical labeling of human airway tree, Tschirren, Juerg, McLennan Geoffrey, Palágyi Kálmán, Hoffman Eric A., and Sonka Milan , IEEE TRANSACTIONS ON MEDICAL IMAGING, 2005///, Volume 24, Issue 12, BALLARD DH, 1982, COMPUTER VISIONBOYDEN EA, 1955, SEGMENTAL ANATOMY LU CARRAGHAN R, 1990, OPER RES LETT, V9, P375 GAREY MR, 1979, COMPUTERS INTRACTABI KITAOKA H, 2002, P MICCAI 2002 TOKYO, P1 MORI K, 2000, IEEE T MED IMAGING, V19, P103 PALAGYI K, 2003, LE, p.1540 - 1547, (2005)
Characterization of the interstitial lung diseases via density-based and texture-based analysis of computed tomography images of lung structure and function, Hoffman, Eric A., Reinhardt Joseph M., Sonka Milan, Simon Brett A., Guo Junfeng, Saba Osama, Chon Deokiee, Samrah Shaher, Shikata Hidenori, Tschirren Juerg, et al. , ACADEMIC RADIOLOGY, 2003///, Volume 10, Issue 10, BAE KT, 1997, RADIOLOGY, V203, P705BENTLEY MD, 1994, CIRC RES, V74, P945 CHULHO W, 2003, J APPL PHYSIOL, V94, P2483 CLARKE LP, 2001, ACAD RADIOL, V8, P447 COXSON H, 2003, AM J RESP CRIT CARE, V167, A81 COXSON H, 2003, AM J RESP CRIT CARE, V167, A81 COXSON, p.1104 - 1118, (2003)
Liver segment approximation in CT data for surgical resection planning, Beichel, Reinhardt, Pock Thomas, Janko Christian, Zotter Roman B., Reitinger Bernhard, Bornik Alexander, Palágyi Kálmán, Sorantin Erich, Werkgartner Georg, Bischof Horst, et al. , Medical Imaging 2004: Image Processing, 2004///, Bellingham; WashingtonScheele, J., Anatomical and atypical liver resection (2001) Chirurg, 72 (2), pp. 113-124;Couinaud, C., (1957) Le Foie - Etudes Anatomiques et Chirurgicales, , Masson, Paris; Strunk, H., Stuckmann, G., Textor, J., Willinek, W., Limit, p.1435 - 1446, (2004)
Assessment of intrathoracic airway trees: Methods and in vivo validation, Palágyi, Kálmán, Tschirren Juerg, Hoffman Eric A., and Sonka Milan , LECTURE NOTES IN COMPUTER SCIENCE, 2004///, Volume 3117, BLAND JM, 1986, LANCET, V1, P307CHEN ZK, 2003, COMPUT MED IMAG GRAP, V27, P469, DOI 10.1016/S0895-6111(03)00039-9 GERIG G, 1993, LECT NOTES COMPUTER, V687, P94 KITAOKA H, 1999, J APPL PHYSIOL, V87, P2207 KONG TY, 1989, COMPUT VISION GRAPH, V48, P357 MADDA, p.341 - 352, (2004)
Quantitative analysis of intrathoracic airway trees: Methods and validation, Palágyi, Kálmán, Tschirren Juerg, and Sonka Milan , INFORMATION PROCESSING IN MEDICAL IMAGING, PROCEEDINGS, 2003///, Berlin; HeidelbergBLAND JM, 1986, LANCET, V1, P307BORGEFORS G, 1984, COMPUT VISION GRAPH, V27, P321 CORMEN TH, 1990, INTRO ALGORITHMS GONZALES RC, 1992, DIGITAL IMAGE PROCES KITAOKA H, 1999, J APPL PHYSIOL, V87, P2207 KONG TY, 1989, COMPUT VISION GRAPH, V, p.222 - 233, (2003)
Quantitative analysis of three-dimensional tubular tree structures, Palágyi, Kálmán, Tschirren Juerg, and Sonka Milan , Medical Imaging 2003, 2003///, Bellingham; Washington, p.277 - 287, (2003)
Kategória: 
Medical Applications
Skeletonization