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Mixed-Integer Evolution Strategies and their Application to Medical Image Analysis

Mixed-Integer Evolution Strategies and their Application to Medical Image Analysis. Jeroen Eggermont Rui Li & Michael Emmerich. Background. Student Computer Science, Leiden University Master Thesis: “Rule-extraction and Learning in the BP-SOM Architecture” PhD Student, Leiden University

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Mixed-Integer Evolution Strategies and their Application to Medical Image Analysis

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  1. Mixed-Integer Evolution Strategies and their Application to Medical Image Analysis Jeroen Eggermont Rui Li & Michael Emmerich

  2. Background • Student Computer Science, Leiden University • Master Thesis: “Rule-extraction and Learning in the BP-SOM Architecture” • PhD Student, Leiden University • PhD Thesis: “Data Mining using Genetic Programming: Classification and Symbolic Regression” • Postdoc, Division of Image Processing, LUMC • NWO-Savage Project Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  3. Overview • LKEB / Division of Image Processing • Atherosclerosis • NWO-Savage Project • Mixed-Integer Evolution Strategies • MIES Extensions • Fitness Based Partitioning • RBFN • Niching Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  4. LKEB / Division of Image Processing • Research entity of the Department of Radiology of the LUMC • The mission is directed towards the research, implementation and validation of automated segmentation, quantification and visualization approaches for structures in medical images • The objective results of these approaches serve to support the clinical decision making processes and the outcomes of clinical research studies Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  5. LKEB / Division of Image Processing • Core competence • Translation of medical imaging problems into clinically applicable technical solutions • Development of scientifically innovative automated analysis methods for medical images • Development of “physician friendly” user interfaces and visualization tools • Extensive clinical validation, beta testing and scientific publication Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  6. Research Development • Hospitals • Research institutes Application Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  7. LKEB: Image Modalities • Röntgen (X-ray) • Echocardiography • IntraVasculaire UltraSound (IVUS) • Magnetic Resonance Imaging (MRI) • Computer Tomography (CT) Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  8. LKEB: Topics • Cardiovascular (heart & bloodvessels) • Lungs (cpod, emphysema) • Orthopedics • Neurology (brains, Alzheimer disease) Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  9. Overview • LKEB • Atherosclerosis • NWO-Savage Project • Mixed-Integer Evolution Strategies • MIES Extensions • Fitness Based Partitioning • RBFN • Niching Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  10. Atherosclerosis Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  11. Atherosclerosis: CTA Analysis Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  12. Atherosclerosis: CTA Analysis Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  13. Lumen Calcified plaque CTA: Detecting Narrowings Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  14. Overview • LKEB • Atherosclerosis • NWO-Savage Project • Mixed-Integer Evolution Strategies • MIES Extensions • Fitness Based Partitioning • RBFN • Niching Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  15. ? Savage Project Feature Detector Images parameter setting Result So you set the first value to 0.1 the second to 42 … Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  16. Savage Project • How to find the optimal parameter setting ? • Trial and Error ? • Educated guess ? • Experience ?  How to optimize a new algorithm ? Solution: Optimize parameters automatically How ?  EvolutionaryAlgorithms Self-Adaptive Vision Agents using Genetic Evolution Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  17. Overview • LKEB • Atherosclerosis • NWO-Savage Project • Mixed-Integer Evolution Strategies • MIES Extensions • Fitness Based Partitioning • RBFN • Niching Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  18. Evolution Strategies: quick overview* • Developed: Germany in the 1970’s • Early names: I. Rechenberg, H.-P. Schwefel • Typically applied to: • numerical optimisation • Attributed features: • fast • good optimizer for real-valued optimisation • relatively much theory • Special: • self-adaptation of (mutation) parameters standard *A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing, Springer, 2003, ISBN 3-540-40184-9 Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  19. Evolution Strategies: technical summary* *A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing, Springer, 2003, ISBN 3-540-40184-9 Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  20. Mixed-Integer Evolution Strategies Why Mixed-Integer Evolution Strategies ?: Image segmentation algorithms have parameters of different types: • Ordinal Discrete {red, green, blue}, {true, false} • Continuous { 3.14, 6.28 } • Integer { 3,18, 42 }  Mixed-Integer Evolution Strategies have Self-Adaptive Mutation operators for each type  MIES outperformsstandard ES on Mixed-Integer Problems Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  21. Mixed-Integer Evolution Strategies Courtesy of Gunther Rudolph Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  22. Mixed-Integer Evolution Strategies CTA Lumen Detector Images parameter solution Expert contours optimal parameter solution Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  23. Mixed-Integer Evolution Strategies Fitness Function for each image I: • resample expert contour E and solution contour C every 2 degrees (180 points per contour) • compute average Euclidean distance between corresponding points Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  24. Mixed-Integer Evolution Strategies Experiments • 9 CTA data sets ( 59-82 images each) • 10 random seeds • (4+28)-MIES (4 parents, 28 offspring) • 50 generations • Comparison to the default parameter settings currently used Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  25. Mixed-Integer Evolution Strategies Conclusion:  MIES works and converges to solutions which are better than the default parameter settings. THERE IS NOT ONE OPTIMAL SOLUTION FOR ALL IMAGES ! Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  26. Overview • LKEB • Atherosclerosis • NWO-Savage Project • Mixed-Integer Evolution Strategies • MIES Extensions • Fitness Based Partitioning • RBFN • Niching Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  27. Fitness Based Partitioning Goal:We want to find optimal parameter settings for all images. • First Idea: Cluster images according to their image segmentation context • Problem: • We don’t know the number of image segmentation contexts • We have no natural distance measure that indicates which images require similar parameter solutions • Second Idea: Co-evolve parameter solutions and image clusters • Problem: • This requires a large number of fitness evaluations  Image segmentation is very time consuming Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  28. Fitness Based Partitioning Group images together that require similar parameter settings for image segmentation Find optimal parameter settings for these groups Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  29. ? ? ? Fitness Based Partitioning Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  30. assign images to partitions CTA Lumen Detector CTA Lumen Detector MI-ES 1 MI-ES 2 best solution best solution CTA Lumen Detector CTA Lumen Detector Fitness Based Partitioning Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  31. Fitness Based Partitioning: Artificial Problems Uniform distribution Gaussian distribution Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  32. Fitness Based Partitioning: Test Results • T = number of MI-ES generations. • Types of Errors: - a sample put on wrong island, or - combined & split partitions Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  33. Fitness Based Partitioning: Potential Problem Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  34. Fitness Based Partitioning: Potential Problem 2 Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  35. Fitness Based Partitioning: Empty Partitions Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  36. Fitness Based Partitioning What to when a partition becomes empty ? • destroy MI-ES population • clone population of the MI-ES algorithm of the largest partition • divide images of largest partition CTA Lumen Detector CTA Lumen Detector MI-ES 1 MI-ES 2 MI-ES 1’ best solution best solution Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  37. Fitness Based Partitioning • Divide images over K partitions • Initialize a MIES algorithm for each partition • for T iterations do • Apply a MIES to each partition for G generations • Select the best MIES solution for each partition • Test the best MIES solution of each partition on all problem instances • Re-assign images to the partition with the best solution • Split partitions if needed Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  38. Fitness Based Partitioning Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  39. Fitness Based Partitioning: Experiments • 9 CTA Data sets • 10 random seeds • (4+28)-MIES algorithm (4 parents, 28 offspring) • each MIES algorithm is run for 5 generations at a time • images are reassigned 10 times Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  40. Fitness Based Partitioning: Results 2-partitions expert contour evolved solution contour best solution MIES 1 best solution MIES 2 Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  41. Fitness Based Partitioning: Results Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  42. Fitness Based Partitioning: Results overall fitness results Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  43. Fitness Based Partitioning: Conclusions • F.B.P. results in parameter settings which lead to better CTA lumen segmentations • Performance assessment: • High reliability: Groups of images usually assigned to the same partition • ….however there remains some sensitivity to the random seed used. Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  44. Overview • LKEB • Atherosclerosis • NWO-Savage Project • Mixed-Integer Evolution Strategies • MIES Extensions • Fitness Based Partitioning • RBFN • Niching Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  45. MIES+RBFN: Time expensive evaluations CTA Lumen Detector Images parameter solution Expert contours optimal parameter solution Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  46. MIES+RBFN • What if we could predict quality of a parameter solution • How ? • Neural Networks (RBFN) • Image processing is time consuming • Every potential parameter solutions must be evaluated Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  47. MIES+RBFN: Train NN CTA Lumen Detector Images parameter solution Expert contours optimal parameter solution Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  48. MIES+RBFN CTA Lumen Detector Images parameter solution optimal parameter solution Expert contours Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  49. MIES+RBFN Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

  50. MIES+RBFN Mixed-Integer Evolution Strategies and their application to Medical Image Analysis

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