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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 Zhanghuizhang@iu.edu

XSEDE'13 San DiegoJuly 24th , 2013

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• Dental caries management project in IUSD

(2010 ~)– Scientific goal: reduce, or reverse the prevalence

of dental caries lesion active → inactive → reversed • Active lesion is a caries lesion that exhibits evidence

of progression for a specific period of time» losing mineral content (or, demineralization)

• Inactive/arrested lesion is a caries lesion that exhibits no evidence of progression for a specific period of time

• Reversed (with treatments)» gaining mineral content (or, remineralization )

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

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

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• The study reported

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

a: Dimension b: Region of interest (ROI)

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

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 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 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 Methods• The Serial Implementation Model

– A small collection of representative dental scans• threshold, valley grayscales, pivot values

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 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 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 Methods• Lesion activity assessment using Map-

Reduce

D ∑ Ii

Di Ii

Ki PhaseID

Vi roiByteArray

Vf 3DModelByteArray

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 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 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• 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 Assessment• 3D-Time Series Visualization

– highlight lesion's volumetric changes B/A treatment

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 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 Assessment• Shape Generation and Depth Measure

– some studies concern finding the association between lesion depth and treatment variables

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• 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• 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

Thank you!Questions?