Download Computational Radiology Approaches in Quantifying Pulmonary Infections in Small Animal Models

Ulas Bagci, PhD Center for Infectious Disease Imaging (CIDI), Radiology and Imaging Sciences, National Institutes of Health (NIH) Bethesda, Maryland ¡  Computational/Quantitative Radiology ¡ 
Automated image analysis and quantification ¡  PET-­‐CT Imaging in Pulmonary Infections ¡  Small animal models: TB, H1N1 Ferret-­‐H1N1 Rabbit-­‐TB ¡ 
Visualization §  Volume §  Surface §  Hybrid ¡ 
What to Measure § 
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Lungs: volume, surface, Airways: tree, thickness Lesions: number and distribution of lesions, volume, ... Activity: functional volume, activity strength, distribution, … Pathology detection-­‐CAD systems §  Mathematical models for organ variation (shape, texture, etc) §  Efficiency and robustness §  To aid radiologists/clinicians in their diagnostic and routine tasks ¡ 
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Human and Small Animal Studies with TB, H1N1,... PET, CT, MRI-­‐PET, PET-­‐CT Imaging Modalities Methodology §  Airway segmentation §  Airway wall thickness estimation §  Cavity detection and segmentation §  PET image segmentation and disease quantification §  Multi-­‐modality co-­‐segmentation (PET-­‐CT, MRI-­‐PET) §  Pathological lung segmentation §  CAD System for Pulmonary Infections ¡ 
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Human and Small Animal Studies with TB, H1N1, … PET, CT, MRI-­‐PET, PET-­‐CT Imaging Modalities Methodology §  Airway segmentation §  Airway wall estimation §  Cavity detection and segmentation §  PET image segmentation and disease quantification §  Multi-­‐modality co-­‐segmentation (PET-­‐CT, MRI-­‐PET) §  Pathological lung segmentation §  CAD System for Pulmonary Infections ¡  Airways §  Commonly related to inflammatory and infectious lung diseases. (Bronchiectasis, etc.) §  Analyzed non-­‐invasively through segmentation on CT scans. 6 ¡  Complex structure (7 h. / scan manually). ¡  Intensity level variance within the lumen. ¡  Similar intensity as adjacent parenchyma. ¡  Blurred airway walls and soft boundaries. 7 ¡  Automatic approach §  Locate trachea root by Hough transform or shape based SVM ¡  Airway enhancement §  Grayscale morphological reconstruction §  Multi-­‐scale vesselness ¡  Fuzzy Connectedness segmentation §  Combined information with affinity function design 8 Seed Generation CT Scan Step 1 Grayscale Morphological Reconstruction Intensity Mapping Multi-­‐scale Vesselness Step 2 Step 3 Hybrid Multi-­‐Scale FC 9 ¡  Data §  EXACT’09 challenge from MICCAI §  40 images, 20 for evaluation §  Covers different diseases ¡  Evaluation §  Visual inspection §  Quantitative measurements §  Organizers accepts binary segmentations and sent back the results with pre-­‐defined evaluation metrics. 10 ¡  Comparison §  Region growing with leakage control §  Voxel classification (method of good performance) Region Growing Best Method in Challenge Organization (ML based) Our method 11 ¡  Comparison §  Region growing with leakage control §  Voxel classification (method of good performance) Comparable Performances. Proposed method does not include any ML algorithm for further refinement! Region Growing Avg: 2 Hours! Avg: 20 Minutes! Best Method in Challenge Organization (ML based) Our method 12 ¡  Wall thickness and dimension measurements are related to various lung diseases §  Bronchiectasis: dilation of airways §  Asthma and bronchitis: wall thickening ¡  Clinical routine §  Manual measurement at limited locations over the CT scan §  Automatic algorithm for computer aided analysis ¡  Lumen segmentation can compromise locally 13 ISBI 2013, MICCAI 2013, and IEEE TMI 2013 Papers -­‐ CIDI 14 ¡  DSCs and HDs were computed with respect to the surrogate truths provided by Observers 1 and 2. DSC (%)
FWHM
Ellipse Fitting
Random Walker
Observer1
57.5
58.6
73.9
Observer2
64.5
65.2
81.4
Observer1
3.56
2.44
1.82
Observer2
3.35
2.25
1.61
Inter Observer
77.6
HD (mm)
1.74
15 Pulmonary tuberculosis is a common infection with high mortality and morbidity (2011 Report of WHO: almost 9 millions active disease, 1.4 millions co-­‐
infected with HIV.) ¡  Extensive necrosis with cavitation is a characteristic feature of TB. ¡  Study of TB cavitation: size, interaction with nearby tissues, and relative positioning with respect to the airways, and functional characterization is often needed for understanding the underlying development of drug resistant TB. ¡  Longitudinal study of TB (spatial) evolution. ¡ 
Medical Physics 2013 -­‐ CIDI 17 ¡  Automatic identification and quantification
18 Lung Region Extraction ¡  Shape-­‐based detection for airway and cavities ¡  Segmentation based on FC
CT Scan Threshold Morphological Reconstruction Connected Component Multi-­‐scale Vesselness Seed: Shape-­‐based Airway & Cavity Detection Cavity Intensity-­‐based FC Hybrid Multi-­‐scale (or Intensity) FC Airway Segmentation Result Local Refinement 19 ¡  Data §  Longitudinal study of 12 rabbits for 7 weeks (JHU, A. Kubler and B. Luna). §  Rabbits imaged in a breath-­‐hold manner §  No consolidation/cavity at baseline ¡  Evaluation §  Detection: manually labeled airway / cavity groups for SVM training and testing §  Segmentation: two manual references
20 ¡  Cavity (spatial) evolution: Small – Growing – Peak -­‐ Shrink
21 22 ¡  Infectious diseases often present as diffuse and multi-­‐focal radiotracer uptake which makes segmentation challenging Focal Multi-­‐Focal/Diffuse CT PET ISBI 2013, IEEE TBME 2013 -­‐ CIDI Week 0 Week 5 Week 10 Week 15 “Z-Stack”
Z Y X X “X-Y plane”
Sampling regimen to capture 4dimensions to establish PET/CT
model of influenza infection in
ferrets
A D C Regions
B Y Hypothesis: Can FDG-­‐PET Imaging be used for Determining disease extent of H1N1? ¡ 
Novel image processing methods were developed, §  “infectious lung diseases” in particular ▪ 
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Airway and airway walls analysis Lung segmentation (volumetric analysis) PET segmentation (functional image analysis) Pathology segmentation (volume and texture analysis) Hybrid visualization Integration of clinical variables with imaging patterns for biomarker discovery ¡  Use of MRI (and MRI-­‐PET) ¡ 
Questions? ¡ 
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Daniel J Mollura Brian Luna Andre Kubler Bappaditya Dey Gabriela Kramer-­‐Marek Awais Mansoor Ziyue Xu Brent Foster Colleen Jonsson William Bishai Sanjay Jain Jayaram K Udupa