Quantitative Analysis of Radiologic Images: Image Segmentation and Registration, Statistical Atlases

1. Quantitative analysis of radiologic images: Image segmentation and registration, statistical atlases Christos Davatzikos, Ph.D. Professor of Radiology Section of Biomedical Image Analysis http://www.rad.upenn.edu/sbia

2. You can control a quantity if you can measure or weigh it Lord Kelvin, 1824-1907 Need to develop tools that obtain accurate and precise measurement from image data

3. Human limitations in measuring: inter-rater differences Expert 2: Total Lesion volume: 7,560 mm^3 Expert 1: Total Lesion volume: 15,635 mm^3

4. Major limitation for: • Diagnosis of disease stage • Monitoring the effect of treatments

5. More human limitations • Quantification/measurement: • ~3% longitudinal atrophy of the hippocampus in early AD patients • Contraction pattern of the cardiac muscle • - a 5% change in radiologic signal could be indicative of evolving pathology

6. Visually detecting morphological abnormalities Scan 1 Scan 2

7. Visually detecting morphological abnormalities Scan 1 Scan 2 30% atrophy!

8. Manual Drawing of anatomical structures Visual evaluation of a 3% atrophy is practically impossible Laborious and not well-reproducible manual outlining is required

9. Even more fundamental limitations of human evaluation • Evaluating complex spatio-temporal patterns of radiologic signal change, especially if the magnitude of the signal change is small and anatomical variability is large Kahneman and Tversky in their Nobel prize winninng careers studied human reasoning under uncertainty and demonstrated the limitations of human reasoning in evaluating conjunctions, i.e. A and B and C …

10. Detecting spatially complex very subtle anatomical abnormalities ? Normal Schizophrenia patient

11. Detecting spatially complex very subtle anatomical abnormalities ? Healthy Mildly Cognitively impaired: Prodromal stage to Alzheimer’s

12. Functional activity during truth telling and lying Lies Truths

13. Brain and criminal behavior

14. Computers can complement and assist humans in many ways

15. Statistical anatomical atlases: from single-individual anatomical examples, to atlases capturing variability in a population Analogous to training of a human reader

16. Disease identification (learn variation of normal anatomy  identify abnormality as a deviation from normal variation) • Integration of data from multiple individuals in order to discover systematic relationships among radiologic and clinical measurements • Does a lesion in a particular part of the brain correlate with a certain neurological deficit? • Does prostate cancer appear uniformly throughout the prostate or does it tend to appear in certain regions more frequently  what is the optimal way of biopsying/treating a patient in order to maximize probability of cancer detection/elimination?) • -What is the normal variation of hippocampal size for a given age? • - What is the normal variation of cardiac shape and deformation?

17. Image Registration: Integration and Comparative Analysis of Images from different individuals / modalities / times /conditions Before Spatial Normalization After Spatial Normalization Underlying biological process that results in abnormal signal, or simply normal tissue whose normal variability, in terms of image properties, needs to be measured Overlay/Comparison of such images? --Image integration and co-registration helps generalize from the individual to the group, and to construct normative data  abnormalities can be distinguished from normal statistical variation

18. Registration and Measurement of Biological Shape  D’Arcy Thompson, 1917:

19. High-Dimensional Shape Transformations ≈ MR image Warped template Template • The deformation function measures the local deformation of the template: Template Shape 1 Shape 2 Red: Contraction Green: Expansion Deformation 1 Deformation 2 Local structural measurements can be measured by analyzing the deformation functions with standard statistical methodologies

21. Significant 4-year GM changes in 107 older adults From the cover page of the Lancet, Neurology

22. Voxel-based analysis of tissue density maps Effect Size Maps RIGHT LEFT NC > FTD NC > AD FTD > AD

23. Tissue atrophy map of an AD patient, relative to cognitively normal controls Patient’s scan Template Space

24. Regions of differences between schizophrenics and normal controls Average of 148 brain images, after deformable registration to the atlas

25. Targeted Prostate Biopsy Using Mathematical Optimization 100 Samples Template Apex Warped Prostate Atlas 7 5 4 1 2 Left 3 Right 6 … Base Atlas with optimal needle positions Apex US prostate image Left Right Deformable Segmentation of Prostate Images Base Segmented 3D Prostate MRI prostate image

26. Quantitative analysis meets visual image interpretation “Older Old Adult” Average Model “Younger Old Adult” Average Model 20 subjects, average age 64.70 20 subjects, average age 83.05 Average age 83 Average age 64.7 20564 mm3 40174 mm3

27. Using a statistical atlas to guide WM lesion segmentation Spatial distribution of WM abnormalities in 50 older adults (BLSA)

28. HAMMER: Hierarchical Attribute Matching Mechanism for Elastic Registration

29. Pattern Matching: Finding Anatomical Correspondences Attribute vector based on wavelet analysis of the anatomical context around each voxel  morphological signature of each voxel

30. Template A brain MRI before warping and after warping

31. HAMMER HAMMER Measuring volumes of anatomical structures : An atlas with anatomical definitions is registered to the patient’s images Model Subject

32. To summarize: • Anatomical definitions are used to create an atlas  analogous to the knowledge of anatomy by humans • Pattern matching performed hierachically at various scales is used to match the atlas to the individual

33. Can we use these quantitative image analysis tools as diagnostic tools? Problem: Potentially high statistical overlap for any single anatomical structure, if disease is not focal -Combine all morphological, physiological, and clinical measurements into a broader phenotypic profile -Use high-dimensional pattern classification and machine learning techniques

34. Where is the problem? Data from Baltimore Longitudinal Study of Aging, Davatzikos et.al. Neurobiology of aging, in press

35. A pattern is sampled by measuring brain volumes and blood flow in a number of brain regions Pattern Classification Abnormality score • Local tissue volumes and PET O15 are combined • 15-20 brain regions (clusters) build a multi-parametric imaging profile

36. Measurement and Integration of Structural and Functional Patterns Abnormality Score

37. Individual Diagnosis • High-dimensional Pattern Classification (Machine learning) • Evaluate spatial patterns of GM, WM, CSF, PET signal distribution • Use these pattern to construct an image-based classifier, using support vector machines w PET-post cingulate L-ERC Anterior L-hipp

38. Brain regions that collectively contributed to classification Images in radiology convention

39. Classification Rate vs. Number of Regions

40. Change of abnormality scores over time * * Clinically normal, has now gone through autopsy with Braak 4 and moderate plaques meets AD pathology criteria After removing this one participant

41. Abnormality scores when converting from normal to MCI Normals: -0.3 MCI at latest scan: 0.26 MCI at year of conversion: 0.15 Already significant structural abnormality on year of conversion to MCI

42. AD vs CN classifier applied to MCI: most MCI’s have AD-like MRI profiles MMSE decline Data from ADNI

43. fMRI for Lie Detection: A Card Concealment Experiment • Experiments performed by the Brain and Behavior Laboratory (Psychiatry) • Particiapnts were asked to lie about the possession of a card of their choice • 22 participants, both true/lie responses • Parameter images were created using the GLM with double gamma HRF

44. Most discriminative brain region: 63.1%

45. Region 2/ Structure 2 P P H H Focal effects Non-focal effects Region1 / Structure 1 The power of true multi-variate analysis vs. mass-univariate

46. Pattern classification results Individual images Average images

47. Statistical maps of group differences Lie Truth Set of regions with predictive power

48. Image2 Image1 Multi-variate analysis continued…….. …..combining different types of images No single image says it all!

49. Computer result by combining 4 different MR acquisition protocols

50. Conclusion • Computers can complement humans in: • Quantification • Increased reproducibility • Analysis of non-focal disease • Evaluating complex spatio-temporal patterns • patterns of longitudinal change of structure and function • patterns of tissue motion and deformation In the heart of computational image analysis is the notion of statistical atlases, which represent normal variation and help identify disease as a deviation from this normal range