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Benefits of texture analysis of dual energy CT for computer-aided pulmonary embolism detection
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Benefits of texture analysis of dual energy CT for computer-aided pulmonary embolism detection

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Pulmonary embolism is an avoidable cause of death if treated immediately but delays in diagnosis and treatment lead to an increased risk. Computer–assisted image analysis of both unenhanced and …

Pulmonary embolism is an avoidable cause of death if treated immediately but delays in diagnosis and treatment lead to an increased risk. Computer–assisted image analysis of both unenhanced and contrast–enhanced computed tomography (CT) have proven useful for diagnosis of pulmonary embolism. Dual energy CT provides additional information over the standard single energy scan by generating four–dimensional (4D) data, in our case with 11 energy levels in 3D. In this paper a 4D texture analysis method capable of detecting pulmonary embolism in dual energy CT is presented. The method uses wavelet–based visual words together with an automatic geodesic–based region of interest detection algorithm to characterize the texture properties of each lung lobe. Results show an increase in performance with respect to the single energy CT analysis, as well as an accuracy gain compared to preliminary work on a small dataset.

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  • 1. Benefits of Texture Analysis of Dual Energy CT for Computer–Aided Pulmonary Embolism Detection A. Foncubierta Rodríguez, O. Jiménez del Toro, A. Platon, P.A. Poletti, H. Müller, A. Depeursinge
  • 2. Pulmonary Embolism • Obstruction of arteries in the lungs • Unspecific symptoms • High mortality rates: – 75% (initial hospital admission) – 30% (3 years after discharge) • Delays in diagnosis increase the risk • But easily treated with anticoagulants 2
  • 3. PE Imaging Material Attenuation Coefficient vs keV 0. 1 1 1 0 1 0 0 40 50 60 70 80 90 100 110 120 130 140 Photon Energy (keV) m(E) (cm2/mg) Iodine Water 80 keV 140 keV Conventional CT images • Wedge shaped regions • Heterogeneous attenuation • Correlation with vascularization and ventilation Dual Energy CT images • 4D Data • X,Y,Z • Energy level • Different materials: different attenuations 3
  • 4. Dataset • 25 patients • Image resolution • 0.83mm/voxel (axial plane) • 1mm inter-slice distance • 1.25mm slice thickness • 11 energy levels • Manually segmented lobes • Qanadli index 4
  • 5. Pipeline • Automatic regions of interest • Region-level features: energy of wavelets • Lobe-level descriptors: Bag of visual words • One vocabulary per energy level 3D Analysis • Histogram of visual words for all energy- level vocabularies • Find optimal combination of energy-level vocabularies 4D data integration: 5
  • 6. Automatic ROIs • Saliency-based: – 3D Difference of Gaussians – Multiple scales – Geodesic regional extrema • Data-driven region shape • Local to global analysis of the lobes 6
  • 7. Region-level Features 4 dimensional feature vector per region Energy in Regions 4 scales 3D DoG 7
  • 8. Bag of visual words • BOVW allows data-driven features: – Patterns actually occurring in the data • Vocabularies – K-means clustering – 5 to 25 words – One vocabulary per energy level – Lobe specific: lobes are not directly comparable • Each lobe described by 11 histograms of VW 8
  • 9. Evaluation • Classification based on 1-NN – Q_i > 0 – Q_i < 0 • Leave One Patient Out • Combinations: – From 1 to 11 energy levels – 5 to 50 visual words per energy level • Reference: 70 KeV for conventional CT 9
  • 10. Results Lobe 4D Analysis Accuracy Energy levels Visual words Conventional Accuracy Lower Right 84% 50+130 KeV 5 52% Lower Left 84% 100+140 KeV 5 48% Middle Right 80% 40+50+130+140 Kev 5 52% Upper Left 76% 40+70+80+90 Kev 25 60% Upper Right 80% 90+120 KeV 25 56% 10
  • 11. Conclusions • Using 4D analysis of DECT outperforms conventional CT: 36% accuracy increase • Consistent results among all lobes • Lobe specificities: – No optimal parameters for all lobes – Methods need to be optimized per lobe • Satisfactory results for integration of automatic ROI detection 11
  • 12. Future work Larger database • Ongoing process Similarity- based retrieval • Qanadli index as metric Optimize BOVW • Synonyms 12
  • 13. Thanks for your attention! Questions? A. Foncubierta-Rodríguez, O. Jiménez del Toro, A. Platon, P.A. Poletti, H.Müller and A. Depeursinge, Benefits of texture analysis of dual energy CT for computer- aided pulmonary embolism detection, in: The 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2013), Osaka 2013