Vessel Tree Segmentation in Presence of Interstitial Lung Disease in MDCT

The automated segmentation of vessel tree structures is a crucial preprocessing stage in computer aided diagnosis (CAD) schemes of interstitial lung disease (ILD) patterns in multidetector computed tomography (MDCT). The accuracy of such preprocessing stages is expected to influence the accuracy of lung CAD schemes. Although algorithms aimed at improving the accuracy of lung fields segmentation in presence of ILD have been reported, the corresponding vessel tree segmentation stage is under-researched. Furthermore, previously reported vessel tree segmentation methods have only dealt with normal lung parenchyma. In this paper, an automated vessel tree segmentation scheme is proposed, adapted to the presence of pathologies affecting lung parenchyma. The first stage of the method accounts for a recently proposed method utilizing a 3-D multiscale vessel enhancement filter based on eigenvalue analysis of the Hessian matrix and on unsupervised segmentation. The second stage of the method is a texture-based voxel classification refinement to correct possible over-segmentation. The performance of the proposed scheme, and of the previously reported technique, in vessel tree segmentation was evaluated by means of area overlap (previously reported: 0.715 ± 0.082, proposed: 0.931 ± 0.027), true positive fraction (previously reported: 0.968 ± 0.019, proposed: 0.935 ± 0.036) and false positive fraction (previously reported: 0.400 ± 0.181, proposed: 0.074 ± 0.031) on a dataset of 210 axial slices originating from seven ILD affected patient scans (used for performance evaluation out of 15). The pro posed method demonstrated a statistically significantly (p <; 0.05) higher performance as compared to the previously reported vessel tree segmentation technique. The impact of suboptimal vessel tree segmentation in a reticular pattern quantification system is also demonstrated.

[1]  Guido Gerig,et al.  Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images , 1998, Medical Image Anal..

[2]  Kaleem Siddiqi,et al.  Flux Maximizing Geometric Flows , 2001, ICCV.

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

[4]  Til Aach,et al.  Fuzzy pulmonary vessel segmentation in contrast enhanced CT data , 2008, SPIE Medical Imaging.

[5]  D Hahn,et al.  Computer-assisted quantification of interstitial lung disease associated with rheumatoid arthritis: preliminary technical validation. , 2009, European journal of radiology.

[6]  E. V. van Beek,et al.  Computer-aided classification of interstitial lung diseases via MDCT: 3D adaptive multiple feature method (3D AMFM). , 2006, Academic radiology.

[7]  Bram van Ginneken,et al.  Toward automated segmentation of the pathological lung in CT , 2005, IEEE Transactions on Medical Imaging.

[8]  S. Sathiya Keerthi,et al.  Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms , 2002, IEEE Trans. Neural Networks.

[9]  S. Armato,et al.  Automated lung segmentation for thoracic CT impact on computer-aided diagnosis. , 2004, Academic radiology.

[10]  Lena Costaridou,et al.  Towards quantification of interstitial pneumonia patterns in lung multidetector CT , 2008, 2008 8th IEEE International Conference on BioInformatics and BioEngineering.

[11]  Lena Costaridou,et al.  Texture classification-based segmentation of lung affected by interstitial pneumonia in high-resolution CT. , 2008, Medical physics.

[12]  Guido Gerig,et al.  Multiscale detection of curvilinear structures in 2-D and 3-D image data , 1995, Proceedings of IEEE International Conference on Computer Vision.

[13]  Qiang Li,et al.  Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans. , 2003, Medical physics.

[14]  Lena Costaridou,et al.  Texture-Based Identification and Characterization of Interstitial Pneumonia Patterns in Lung Multidetector CT , 2010, IEEE Transactions on Information Technology in Biomedicine.

[15]  SonkaMilan,et al.  Segmentation of pulmonary vascular trees from thoracic 3D CT images , 2009 .

[16]  A. Nicholson,et al.  HRCT diagnosis of diffuse parenchymal lung disease: inter-observer variation , 2004, Thorax.

[17]  Richard A. Robb,et al.  High resolution multidetector CT aided tissue analysis and quantification of lung fibrosis , 2006, SPIE Medical Imaging.

[18]  F. Yin,et al.  Investigation of the support vector machine algorithm to predict lung radiation-induced pneumonitis. , 2007, Medical physics.

[19]  B. van Ginneken,et al.  Computer-aided diagnosis in high resolution CT of the lungs. , 2003, Medical physics.

[20]  Alejandro F. Frangi,et al.  Muliscale Vessel Enhancement Filtering , 1998, MICCAI.

[21]  Gady Agam,et al.  Vessel tree reconstruction in thoracic CT scans with application to nodule detection , 2005, IEEE Transactions on Medical Imaging.

[22]  Ioannis Mariolis,et al.  Computer Aided Diagnosis of Diffuse Lung Disease in Multi-detector CT – Selecting 3D Texture Features , 2010 .

[23]  William E. Higgins,et al.  Multi-generational analysis and visualization of the vascular tree in 3D micro-CT images , 2002, Comput. Biol. Medicine.

[24]  Carl-Fredrik Westin,et al.  Tissue Classification Based on 3D Local Intensity Structures for Volume Rendering , 2000, IEEE Trans. Vis. Comput. Graph..

[25]  Lubomir M. Hadjiiski,et al.  Automatic multiscale enhancement and segmentation of pulmonary vessels in CT pulmonary angiography images for CAD applications. , 2007, Medical physics.

[26]  Nicholas Ayache,et al.  Model-Based Detection of Tubular Structures in 3D Images , 2000, Comput. Vis. Image Underst..

[27]  Joseph M. Reinhardt,et al.  Anatomy-Guided Lung Lobe Segmentation in X-Ray CT Images , 2009, IEEE Transactions on Medical Imaging.

[28]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[29]  Yoshinobu Satoy Carl-Fredrik Westiny Abhir Bhaleraoy Shin Nakajimay,et al.  Tissue Classi cation Based on 3 D Local Intensity Structure for Volume Rendering , 1997 .

[30]  Anthony Ralston,et al.  Statistical Methods for Digital Computers. , 1980 .

[31]  E. Hoffman,et al.  Mass preserving nonrigid registration of CT lung images using cubic B-spline. , 2009, Medical physics.

[32]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[33]  L. Costaridou,et al.  Combining 2D wavelet edge highlighting and 3D thresholding for lung segmentation in thin-slice CT. , 2007, The British journal of radiology.