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20080125 Friday Food

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Quantitative analysis of ultrasound imagesof the preterm brain

Ewout Vansteenkiste - IBBT-Medisip-UGent

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20080125 Friday Food

  1. 1. Quantitative analysis of ultrasound images of the preterm brain Ewout Vansteenkiste IBBT-Medisip/IPI-UGENT Friday Food 25/01/2008
  2. 2. Outline [Source: William Lawson, A new Orchard and Garden , 1648, Londen ] quantitative image analysis medical ultrasound speckle-reduction in ultrasound 2D echo/3D MRI registration white matter classification 1/24 texture-classification psycho-physics segmentation registration white matter segmentation ventricle segmentation segmentation carotid
  3. 3. Quantitative image analysis 2/24 tumor = “white dot” in the image size = “small”, “average” Qualitative analysis : in words 2.25 cm² Both experts measure the tumor Using the same segmentation algorithm Quantitative analysis: through measuring
  4. 4. Medical ultrasound 3/24 SPECKLE probe electric current Pi ëzo-electric cristal pulsing Tissue structures/transitions skin
  5. 5. White matter damage = Periventricular Leukomalacia (PVL) grey matter: what we think with white matter: highways of the brain ventricles: cavities regulating brain fluid 4/24 <ul><li>Preterm infants born </li></ul><ul><li>between week 26 and 32 </li></ul><ul><li>- Very low birth weight </li></ul><ul><li>(<1500g) </li></ul><ul><li>- Lack of oxygen = increased </li></ul><ul><li>chance on brain damage </li></ul>mild severe
  6. 6. White matter damage diagnosis <ul><li>critical condition preterms: incubated/ventilated </li></ul><ul><li>acquiring high-quality scans (MRI) is not trivial </li></ul><ul><li>in first days of life (starts only at day 3) </li></ul><ul><li>fontanelle open = early echo is possible </li></ul><ul><li>early detection is important </li></ul>pros: - non-invasive/safe - portable - real-time imaging - relatively cheap contras: - poor image quality - diagnosis = subjective 5/24 “ flaring” normal severe mild flaring
  7. 7. Problem = subjectivity <ul><li>- Flaring (areas) difficult </li></ul><ul><li>to describe </li></ul><ul><li>Not easy to delineate </li></ul><ul><li>objectively </li></ul><ul><li>- Qualitative diagnosis </li></ul><ul><li>insufficient </li></ul><ul><li>- Quantitative objective </li></ul><ul><li>results? </li></ul><ul><li>1) flaring? </li></ul><ul><li>2) how widely spread? </li></ul>6/24
  8. 8. Tissue texture classification (1) - Texture : no unique definition. Description: regular, irregular, stochastic pattern present in most natural scenes: - Texture parameters : mathematical measures expressing texture characteristics 7/24 <ul><li>Scale determines texture: </li></ul>- Important for ultrasound: tissue structure is manifested as speckle texture
  9. 9. Example texture parameters: co-occurrence matrices wood cloth 8/24 Contrast = 100 Entropy = 0.78 Contrast = 60 Entropy = 0.34 pathological benign Contrast = 70 Entropy = 0.44 Contrast = 130 Entropy = 0.64 255 0 255 0 2D co-occurrence matrix
  10. 10. Tissue texture classification (2) 9/24 length width length width length width ? ?
  11. 11. Tissue texture classification (3) Quantitative analysis = precision 92.5% = sensitivity 88 % Qualitative analysis = precision 75% = sensitivity 70% 10/24 patholigical non-pathological 140 pati e nts <ul><li>Machine-settings: </li></ul><ul><li>Gain </li></ul><ul><li>Power </li></ul><ul><li>Time Gain Compensation </li></ul>Compensation algorithm <ul><li>Texture parameters: </li></ul><ul><li>Skewness </li></ul><ul><li>Contrast </li></ul><ul><li>Angular second moment </li></ul><ul><li>X-gradient </li></ul><ul><li>Y-gradient </li></ul>Bayes classifier KNN classifier classifier combination - pathological - non-pathological Normalization LDA classifier
  12. 12. Outline [Source: William Lawson, A new Orchard and Garden , 1648, Londen ] Quantitative image analysis medical ultrasound Texture classification white matter classification 11/24
  13. 13. Flare segmentation and area estimation 12/24 sensitivity 98% Validation? Initial texture- Basd segmen- Tation map -Morfological closing -Gradient -Opening by Reconstruction expert existing new Expert delineation: subjective? 2D US 3D MRI registration
  14. 14. Multimodal image registration <ul><li>idea = “1 + 1 adds up to more than two” </li></ul><ul><li>registering = aligning images so that </li></ul><ul><li>relevant structures overlap </li></ul><ul><li>multimodal = images of different </li></ul><ul><li>image modalities, CT, MRI, echo </li></ul><ul><li>- fusion = the result after registration </li></ul>Magnetic Resonance Scanner <ul><li>+ 3D high-quality imaging </li></ul><ul><li>limited scan time resulting in </li></ul><ul><li>low axial-resolution </li></ul>13/24 + 2D high-resolution - low image quality
  15. 15. Multimodal 2D ultrasound to 3D MRI registration initialization Mutuel Information Metric Regular Step Gradient Descent Rigid Transformation Trilinear Interpolation result 14/24
  16. 16. Validation registration = “CAVE” + segmentation 15/24 Registration algorithm flaring segmentation MRI-flaring expert
  17. 17. Segmentation extended: ventricles + carotid enlarged ventricles indicative for PVL 3D reconstruction 2D seg- mentation 16/24 Bifurcation of the carotid: atherosclerosis 3D reconstruction [Source = Glor, 2004] 2D seg- mentation
  18. 18. Outline [Source: William Lawson, A new Orchard and Garden , 1648, Londen ] quantitative image analysis medical ultrasound Texture-classification segmentation registration 2D echo/3D MRI registration white matter classificaton 17/24 white matter segmentation ventricle segmentation segmentation carothid
  19. 19. Psychovisual experiments SUBJECTS dummy? physicians? Experts? METHODOLOGY 18/24 STIMULI
  20. 20. Image degradations <ul><li>- Increasing amount of artefact removing algorithms: </li></ul><ul><li>Problem: how to assess the quality of degraded/filtered images </li></ul>19/24 blur noise artefacts
  21. 21. Test room implementation examples (1) © Cedric Marchessoux - BARCO 20/24
  22. 22. Set-up experiment: filtering of ultrasound images <ul><li>Can we facilitate a qualitative diagnosis by speckle suppression? </li></ul><ul><li>GenLik-methode developed at our department adapted to ultrasound images </li></ul><ul><li>Improved technique applied successfully on quantitative registration and segmentation tasks </li></ul>[bron: Pizurica, 2002] 21/24 original filtered
  23. 23. methodology + results <ul><li>- Different scenes (stimuli) were selected containing diverse image structures and 4 levels of filtering: </li></ul><ul><li>Given the difficulty to differentiate noise from relevant information, following question was posed to the experts involved in the experiment : </li></ul><ul><li>Which image do you prefer in terms of diagnostic image quality (discrete scale -3 to 3) </li></ul>22/24 speckle reduction rejected ! Les suited for diagnosis degree of filtering 0 1 2 3 4
  24. 24. Conclusion <ul><li>- Classification algorithm for white matter </li></ul><ul><li>damage </li></ul><ul><li>Segmentation algorithm to estimate flaring area </li></ul><ul><li>(in practice since 2005) </li></ul><ul><li>Registration algorithm to align 2D ultrasound images and 3D MRI data </li></ul><ul><li>Ventricle segmentation </li></ul><ul><li>Segmentation algorithm carotid </li></ul><ul><li>Psycho-visual experiment on speckle suppression </li></ul>23/24 Improved, early quantitative detection of PVL sensitivity: 70% 98% + ultrasound more sensitive than MRI
  25. 25. and remember… The more questions you now ask, the less time we have to eat… 24/24

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