Segmentation Trainer A Robust and User-friendly Machine Learning Image Segmentation Solution

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Georgiana Phillips
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1 Segmentation Trainer A Robust and User-friendly Machine Learning Image Segmentation Solution Presented by Mike Marsh, Ph.D. Dragonfly Product Manager Thursday, March 2, th FIB-SEM Users Group Meeting Gaithersburg, MD

2 About ORS Headquartered in Montreal, Canada. Founded in Registered users in 80 countries. Practicing ISO and IEC standards compliant processes Visual SI ORS Visual

3 About ORS Headquartered in Montreal, Canada. Founded in Registered users in 80 countries. Practicing ISO and IEC standards compliant processes Dragonfly ORS Visual

4 About Dragonfly Rapid Innovation V2.0 launched September 2016 V2.1 coming in April 2017 macro engine superpixel segmentation machine learning segmentation engine In-application store V2.2 coming in fall 2017 (coming to Linux) Technology Anaconda Python 3.5 for scientific computing State-of-the-art image segmentation High-impact rendering engine Extensibility and Community Sockets for extensions: Embedded online console Object analysis measurements Image filters Menu-actions Macros Machine Learning classifiers (and more) App store for sharing and versioning Licensing Flexible licensing options for various institutional needs Free licensing for non-commercial use in most countries

5 Image Segmentation The hard way and the easy way Painstaking: Painting Constrained Painting Threshold-gated painting Superpixel-bloc painting Easy, but never good enough: Point-and-click Thresholding (interactive) Thresholding (algorithmically, eg. Otsu s method) Other tools Automated?

6 Black box classifier segmentation Input image Signal Textures Filter bank (Feature Presets) Spatial Discretization Smart Grid (Region) Engine Parameters Segmentation Classifier Class 1 Class 2 Machine learning core Engine Parameters

7 Black box classifier segmentation FIB-SEM of fuel cell Signal Textures Filter bank (Feature Presets) Classifier Machine learning core Engine Parameters Spatial Discretization Smart Grid (Region) Engine Parameters Segmentation Electrolyte Electrode Pore space

8 Black box classifier segmentation FIB-SEM of fuel cell Signal Textures Filter bank (Feature Presets) Classifier Machine learning core Engine Parameters Spatial Discretization Smart Grid (Region) Engine Parameters Segmentation Electrolyte Electrode Pore space

9 Filter Banks Use any of the filters in the Image Processing toolbox Smoothing Edge Enhancement Texture Gabor HoG DoG Standard deviation Aggregate into filter banks

10 Spatial Discretization

11 Spatial Discretization

12 Spatial Discretization

13 Spatial Discretization Pixel classification SmartGrid cell classification: Superpixel Watershed on Grid Superixel (Scikit-learn) Watershed on Grid (Scikit-learn)

14 Machine Learning Core Random Forest Extra-Trees Adaboost Gradient Boosting Bagging K-Nearest Neighbors

15 Black box classifier segmentation FIB-SEM of fuel cell Signal Textures Filter bank (Feature Presets) Classifier Machine learning core Engine Parameters Spatial Discretization Smart Grid (Region) Engine Parameters Segmentation Electrolyte Electrode Pore space

16 Black box classifier segmentation SE Signal Textures Filter bank (Feature Presets) Spatial Discretization Smart Grid (Region) Engine Parameters Segmentation BSE Classifier Machine learning core Engine Parameters Electrolyte Electrode Pore space

17 Black box classifier segmentation SE Signal Textures Filter bank (Feature Presets) Spatial Discretization Smart Grid (Region) Engine Parameters Segmentation BSE Classifier Machine learning core Engine Parameters Electrolyte Electrode Pore space

18 Train it Electrolyte Electrode Pore space SE image BSE image Filter bank (Feature Presets) Spatial discretization settings Smart Grid (Region) engine Parameters Classifier Machine learning core Engine Parameters Segmentation Electrolyte Electrode Pore space

19 Apply it SE image BSE image Filter bank (Feature Presets) Spatial discretization settings Smart Grid (Region) engine Parameters Classifier Machine learning core Engine Parameters Segmentation Electrolyte Electrode Pore space

20 Apply it Mask SE image BSE image Filter bank (Feature Presets) Spatial discretization settings Smart Grid (Region) engine Parameters Classifier Machine learning core Engine Parameters Segmentation Electrolyte Electrode Pore space

34 It s modular SE image Signal Textures Filter bank (Feature Presets) Spatial Discretization Smart Grid (Region) Engine Parameters Segmentation BSE image Classifier Machine learning core Engine Parameters Electrolyte Electrode Pore space

35 It s modular (Deep Learning CNN) Late 2017 SE image Signal Textures Not necessary Spatial Discretization Not necessary Segmentation BSE image Deep Learning core Engine Parameters Classifier Electrolyte Electrode Pore space

36 Encourage re-use of Classifiers Share classifiers with the community in the App Store (Infinite Toolbox) April 2017 Preview classifiers online Late 2017

37 Acknowledgments Isabelle Bouchard Nicolas Piche scikit-learn.org (Machine Learning in Python)

39 Workflow for Using Classifiers Build the classifier Train it Tune it Re-use it

40 Workflow for Using Classifiers Build the classifier Train it Tune it Iterate: Update training classes Tweak engine parameters Add / remove filter banks review coefficients Retrain Preview Re-use it

41 Segmenting Systematically (and with multiple signals) 1D thresholding: Use range 2D thresholding: Histographic segmentation 3D, 4D,... :??? BSE, ESB Elemental maps: Cu, Mb, Sn, Ni, More common than that: beyond simple signal intensity, you may have spatially correlated signal (e.g. texture)

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