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

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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


Xsede enabled high throughput caries lesion activity assessment

Thank you!

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