xsede enabled high throughput caries lesion activity assessment
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XSEDE-enabled High-throughput Caries Lesion Activity Assessment. Hui Zhang, Guangchen Ruan, Hongwei Shen, Michael Boyles, Huian Li, Masatoshi Ando. Hui Zhang [email protected] XSEDE\'13 San Diego July 24 th , 2013. Outline. Background What is caries lesion activity

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xsede enabled high throughput caries lesion activity assessment

XSEDE-enabled High-throughput Caries Lesion Activity Assessment

Hui Zhang, Guangchen Ruan, Hongwei Shen, Michael Boyles, Huian Li, Masatoshi Ando

Hui Zhang

[email protected]

XSEDE\'13 San Diego

July 24th , 2013

outline
Outline
  • Background
    • What is caries lesion activity
    • Scientific goal and computing objective
  • Dataset and Methods
    • Computing task implemented in a serial means
    • How Map-Reduce framework can be applied
  • Assessment Examples
    • Visualization and analysis
    • Qualitative and quantitative lesion activity assessment
  • Conclusion and Future Work
introduction
Introduction
  • Dental caries management project in IUSD (2010 ~)
    • Scientific goal: reduce, or reverse the prevalence of dental caries lesion

active → inactive → reversed

      • Activelesion is a caries lesion that exhibits evidence of progression for a specific period of time
          • losing mineral content (or, demineralization)
      • Inactive/arrestedlesion is a caries lesion that exhibits no evidence of progression for a specific period of time
      • Reversed (with treatments)
          • gaining mineral content (or, remineralization )
introduction1
Introduction
  • Lesion activity assessement (arrested or active) is important
      • essential and critical in dental studies
      • critical impact on dental treatment decision-making
      • incorrect determination can easily result in wrong treatment
introduction2
Introduction
  • But …….

Today in dental clinical practice visual and tactile inspections are commonly used :

      • subjective
      • dependent on observer\'s

experience to be accurate

      • results often in-consistent
        • tracking
        • temporal comparison

Visual Assessment

Tactile Sensation

introduction3
Introduction
  • (Dental) Computing objective
      • Bring computers and computing technologies to dentistry research
        • dental imaging technology

(µ-CT imaging→ cross-sectional dental scans)

        • image segmentation

(cross-sectional scans→ ROIs)

        • visualization and analysis

(lesion activity assessment → 3D-time series analysis)

      • Design methods not only for "marking" on dental scans, but also quantifying the volumetric information in the assessment
      • Use HPC and parallel computing to scale to larger datasets
datasets and methods

a: Dimension

b: Region of interest (ROI)

Schematic diagrams showing specimen dimension (a), and region of interest (b).

Datasets and Methods
  • The study reported

195 ground/polished 3x3x2mm blocks prepared from extracted human teeth collected from Indiana dental practitioners (approved by IU IRB#0306-64)

datasets and methods1
Datasets and Methods
  • Longitudinal dental experiment
    • uses 5-phase dem./rem. model
    • healthy1→dem2 →dem3→dem4 →rem5
    • temporal evaluation
      • U-CTs
      • specimen/phase
datasets and methods2
Datasets and Methods
  • µ-CT Dental Scans
  • ~1000 scans per specimen per time point
  • each u-CT scan
    • 16-bit gray-scale image
    • 1548×1120 resolution
    • ~1.65 MB size
    • lesion on u-CT scan shows

observable gray-scale

difference

datasets and methods3
Datasets and Methods
  • 3D-Time Series Analysis Workflow (to quantify and compare volumetric lesion information over time)
  • Pre-analysis training
    • threshold, pivot values (based on histograms)
  • Region-of-interest (ROI) segmentation
    • blob detection, morphological operation
  • 3D construction
    • stacking ROIs, generating isosurface and

geometry

  • Visual analysis (on volumetric models)
    • temporal comparison
      • How lesion evolves on same specimen
    • cross-conditional comparison
      • How lesion evolves with different treatments
datasets and methods4
Datasets and Methods
  • The Serial Implementation Model
  • A small collection of representative dental scans
    • threshold, valley grayscales, pivot values
datasets and methods5
Datasets and Methods
  • The Serial Implementation Model
  • A small collection of representative dental scans
    • threshold, pivot values
  • Segment ROIs on all scans (with established parameters)
    • binary image conversion
    • apply morphological operations (erosion and dilation) to remove false ROI candidates
    • blob detection → ROI boundary
    • processing images to keep only relevant pixels
datasets and methods6
Datasets and Methods
  • The Serial Implementation Model
  • Select representative dental scans
    • Threshold, pivot values
  • Segment ROIs on all scans
    • binary image conversion
    • apply morphological operations (erosion and dilation) to remove false ROI candidates
    • blob detection → ROI boundary
    • processing images to keep only relevant pixels
  • 3D construction
    • stack ROIs and visual analysis
datasets and methods7
Datasets and Methods
  • The Parallel Model
    • MapReduce - center around 2 func. to represent domain problems
    • General pattern

Map(Di) → list(Ki,Vi); Reduce(Ki, list(Vi)) → list(Vf)

    • Divide the dataset D into individual data values Di
    • Map(Di)is applied to each individual value, producing many lists of key value pairs list(Ki,Vi)
    • Data produced by Map operations will be grouped by key Ki, producing associated values list(Vi)
    • Reduce(Ki, list(Vi))takes each key Ki and associated list of values list(Vi) to produce a list of final output values
datasets and methods8
Datasets and Methods
  • Lesion activity assessment using Map-Reduce
  • Map(Di) → list(Ki,Vi):
  • performs ROI segmentation;
  • extract image phaseID (encoded in filename);
  • produce (phaseID, roiByteArray) as key-value pair
  • Reduce(Ki, list(Vi)) → list(Vf) :
  • receives ROI collections keyed to phaseID;
  • performs 3D construction;
  • produce (phaseID, 3DModelByteArray) pair
datasets and methods9
Datasets and Methods
  • Better performance with sequence files and data compression
    • Hadoop excels in processing small # of large files
    • Too many I/O operations → extra burden
    • Implementation
      • Data packing before

3D-time series workflow

      • Map task loads images
      • Reduce task
        • produce sequence files
        • apply compression
datasets and methods10
Datasets and Methods
  • Computing setup and parameters
    • 64-node cluster on SDSC-Gordon
      • 8 Map slots 4 Reduce slots
    • Used DEFLATE codec and block compression for sequence files
    • 40,000 images in 12.62 minutes
    • More performance and scalability data reported in “ Exploting MapReduce and Data Compression for Data-intensive Applications“
lesion activity assessment
Lesion Activity Assessment
  • Quantitative Assessment
    • lesion and its volumetric change measured in pixel^3
    • objective and consistent comparisons across specimen and across different experimental conditions
    • scalable to larger

datasets

lesion activity assessment1
Lesion Activity Assessment
  • 3D-Time Series Visualization
    • highlight lesion\'s volumetric changes B/A treatment
lesion activity assessment2
Lesion Activity Assessment
  • 3D-Time Series Visualization
    • show lesion\'s volumetric changes B/A treatment
    • combine dem. and rem.

enamel in an integrated view

with transparency

lesion activity assessment3
Lesion Activity Assessment
  • Shape Generation and Depth Measure
    • some studies concern finding the association between lesion depth and treatment variables

previous effort:

approximate lesion depth based grayscale on QLF images

lesion activity assessment4
Lesion Activity Assessment
  • Shape Generation and Depth Measure
    • some studies concern finding the association between lesion depth and treatment variables
lesion activity assessment5
Lesion Activity Assessment
  • Shape Generation and Depth Measure
    • some studies concern finding the association between lesion depth and treatment variables
    • 3D Poisson surfaces constructed for interactive depth measurement and comparison
conclusion
Conclusion
  • Dental computing gives rise to a broad range of educational and treatment planning applications for dentistry;
  • A promising research approach that allows users to use imaging technology, computational algorithm, and visualization methods to make lesion activity assessment faster and more accurate;
  • The workflow can be supported computationally; implemented using parallel programming model such as MapReduce; further automated using HPC resources.
future work
Future Work
  • Provide templates to other domains with similar computing task
  • Potential improvement of the workflow
    • The final result is much lighter compared to raw inputs
      • Data transfer with ROI boundary vectors instead of heavy image arrays
      • Compression of intermediate analysis results
slide26

Thank you!

Questions?

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