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Comprehensive Framework for Automated Image Analysis and Segmentation Using Crowdsourced Data

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This project develops a systematic comparison framework for evaluating image segmentation and analysis algorithms leveraging crowdsourced data. Utilizing tools like the SCORE system, it integrates automatic segmentation, diverse image repositories, and reports on algorithm effectiveness. The infrastructure facilitates parameter tuning, storage, and sharing of large, well-curated image data sets across multiple imaging modalities, including MRI, CT, and PET. A key focus is ensuring the development of ground truth data for rigorous assessment and improvement of imaging algorithms in a user-friendly environment.

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Comprehensive Framework for Automated Image Analysis and Segmentation Using Crowdsourced Data

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  1. Group 4: Web based applications/ crowdsourcing Marcel Prastawa ZivYaniv Patrick Reynolds Stephen Aylward Sean Megason

  2. A2D2s • SCORE: Systematic Comparison through Objective Rating and Evaluation (Prastawa): • SCORE++: Crowd sourced data, automatic segmentation, and ground truth for ITK4 (Megason): • Framework for automated parameter tuning of ITK registration pipelines (Yaniv)

  3. Overall Goals • Scoring filters- segmentation, tracking, registration algorithms • Image repository – small, well curated, diverse collection with ground truth • Infrastructure – test data IO, algorithm quality dashboard, grand challenge, crowd-sourced ground truth

  4. Requisite Architecture Slide SCORE Server Dashboard MIDAS Image Repository Scoring Insight Journal Images Algorithms ITK

  5. New features, filters, classes • ITK Classes • ITK Reader and Writer for MIDAS • InTotoImageData3DSource for synthetic data • Scoring filters- surfaces, volumes • Parameter tuning- Nelder-Mead, Particle Swarm • Track(?) • MIDAS extensions • Image sets • SCORE : A new MIDAS instance

  6. New data to be released • Number – 10 image sets • Size – large (10-100GB) • How to share – via SCORE respository • Diverse imaging modalities and image analysis challenges • Confocal, 2-photon, phase, MRI, CT, PET,

  7. How data will be released • MIDAS – manual download • itkReader

  8. Tiers of Data • Thumbnail • Toy • Training • Challenge • Raw • Ground truth segmentation • User segmentation(?) X

  9. License • Database: Open Data Commons - Database Contents License v1.0 • Image sets within Database: Open Data Commons Attribution License • Signed by PI and Harvard Office of Technology Transfer

  10. Confocaltimelapse zebrafish development – segmentation and tracking

  11. PET-MRI of mouse cancer model - segmentation and registration

  12. Security • Raw Data • Upload restricted to small group for SCORE++ repository • Download – anonymous • Segmented Data (crowd source) • Upload - registered users • Download - anonymous • Challenge testing • Registered users, run on VM

  13. Metadata Must balance completeness with ease-of-use • Small set of structured data – image itself • Unstructured data as in methods section of paper – experiment, image acquisition • Biological question / image analysis challenge

  14. Ground truth • Only exists for synthetic data • ImageReaderInTotoSource • Model cell shape, distribution, division • Model imaging via a microscope (PSF, noise) • Output simulated 4D image set plus ground truth

  15. Manual Segmentation • Done client side using their own apps (Slicer, GoFigure…) • Label map image

  16. Dashboard of Algorithms Will show • Image set • Algorithm • Parameter • Score • Details

  17. Grand Challenge Framework • Upload algorithm • ITK source code • Executable • Runs in VM with MIDAS • Scoring • Code private for scoring • Dashboard • Code published as IJ article as part of competition

  18. Problems • Transfer speeds over internet • No ground truth • Parameters for segmentation filters • Parameters for scoring filters

  19. Plan of action • Setup authoritative instance of MIDAS at NLM

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