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© Trustees of Indiana University Released under Creative Commons 3.0 unported license; license terms on last slide. XSEDE-enabled High-throughput Caries.

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Presentation on theme: "© Trustees of Indiana University Released under Creative Commons 3.0 unported license; license terms on last slide. XSEDE-enabled High-throughput Caries."— Presentation transcript:

1 © Trustees of Indiana University Released under Creative Commons 3.0 unported license; license terms on last slide. XSEDE-enabled High-throughput Caries Lesion Activity Assessment Hui Zhang, Guangchen Ruan, Hongwei Shen, Michael Boyles, Huian Li, Masatoshi Ando Hui Zhang huizhang@iu.edu XSEDE'13 San Diego July 24 th, 2013

2 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

3 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 )

4 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

5 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

6 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

7 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: Dimensionb: Region of interest (ROI) Schematic diagrams showing specimen dimension (a), and region of interest (b).

8 Datasets and Methods Longitudinal dental experiment uses 5-phase dem./rem. model healthy 1 →dem 2 →dem 3 →dem 4 →rem 5 temporal evaluation – U-CTs – specimen/phase

9 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

10 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

11 Datasets and Methods The Serial Implementation Model – A small collection of representative dental scans threshold, valley grayscales, pivot values

12 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

13 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

14 Datasets and Methods The Parallel Model MapReduce - center around 2 func. to represent domain problems General pattern Map(D i ) → list(K i,V i ); Reduce(K i, list(V i )) → list(V f ) Divide the dataset D into individual data values D i Map(D i ) is applied to each individual value, producing many lists of key value pairs list(K i,V i ) Data produced by Map operations will be grouped by key K i, producing associated values list(V i ) Reduce(K i, list(V i )) takes each key K i and associated list of values list(V i ) to produce a list of final output values

15 Datasets and Methods Lesion activity assessment using Map-Reduce D∑ I i DiDi IiIi KiKi PhaseID ViVi roiByteArray VfVf 3DModelByteArray Map(D i ) → list(K i,V i ): performs ROI segmentation; extract image phaseID (encoded in filename); produce (phaseID, roiByteArray) as key-value pair Reduce(K i, list(V i )) → list(V f ) : receives ROI collections keyed to phaseID; performs 3D construction; produce (phaseID, 3DModelByteArray) pair Map(D i ) → list(K i,V i ): performs ROI segmentation; extract image phaseID (encoded in filename); produce (phaseID, roiByteArray) as key-value pair Reduce(K i, list(V i )) → list(V f ) : receives ROI collections keyed to phaseID; performs 3D construction; produce (phaseID, 3DModelByteArray) pair

16 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

17 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“

18 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

19 Lesion Activity Assessment 3D-Time Series Visualization – highlight lesion's volumetric changes B/A treatment

20 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

21 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

22 Lesion Activity Assessment Shape Generation and Depth Measure – some studies concern finding the association between lesion depth and treatment variables

23 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

24 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.

25 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

26 Thank you! Questions?


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