Difference between revisions of "2016 Winter Project Week/Projects/Cluster-Driven Lung Segmentation"

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Image:PW-MIT2016.png|link=2016_Winter_Project_Week#Projects|[[2016_Winter_Project_Week#Projects|Projects List]]
 
Image:PW-MIT2016.png|link=2016_Winter_Project_Week#Projects|[[2016_Winter_Project_Week#Projects|Projects List]]
 
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==Project Description==
 
==Project Description==
 
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! style="text-align: left; width:27%" |  Objective
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! style="text-align: left; width:27%" |  Approach and Plan
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! style="text-align: left; width:27%" |  Progress and Next Steps
 
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! style="text-align: left; width:27%" |       Objective
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<!-- Objective bullet points -->
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* Consolidation of Lung Nodule detection and segmentation algorithms into pipelines.
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* Iterative parameter-space testing of pipelines in cluster-computing environments.
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* Perform evaluations against expert-contoured segmentations.
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* Test and run automated lung nodule segmentation pipelines using cluster-computing environments
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* Explore and optimize existing Lung Nodule detection/segmentation tools.
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* Evaluate outputs using metrics such as Dice, Hausdorff, or Radiomics.  
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* Configure and launch StarCluster Nodes on AWS to run pipelines.
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* Use LIDC Data for evaluation of pipelines (~1200+ CT Images with manual contours).
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! style="text-align: left; width:27%" |      Approach, Plan
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* Evaluate Segmentation pipelines against manual contours  using metrics such as SimpleITK filters, Dice, Hausdorff, and Radiomics.  
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* Use LIDC Data
 
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! style="text-align: left; width:27%" |     Progress
 
 
<!-- Fill this out at the end of Project Week; describe what you did this week and what you plan to do next -->
 
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* StarCluster!
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* Exploring the Lesion-Size Toolbox algorithms for Lung Nodule detection.
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* Configured StarCluster/AWS testing pipeline with time and cost estimates
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* Confirmed that Slicer running in Docker container running on AWS EC2 instance can successfully host CIP algorithms.  [https://www.youtube.com/watch?v=tCCQ_N2m8zs Video demonstrates running node segmentation and feature analysis running in a browser].
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* Exploring use scenarios for radiomic analysis
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== References ==
  
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==Background and References==
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[https://docs.google.com/presentation/d/1miyj0_l9x2Z3oMSnpB3beoLTy12iUo0ilp7f5hhh8dk/edit#slide=id.g74ed54aa6_0_95 Slides describing initial experiments]
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<!-- Use this space for information that may help people better understand your project, like links to papers, source code, or data -->
 

Latest revision as of 15:45, 8 January 2016

Home < 2016 Winter Project Week < Projects < Cluster-Driven Lung Segmentation

Key Investigators

  • Vivek Narayan (Dana Farber)
  • Raúl San José (BWH)
  • Dan Blezek (Isomics, Inc.)
  • Steve Pieper(Isomics, Inc.)
  • Chintan Parmar (Dana Farber)

Project Description

Objective Approach and Plan Progress and Next Steps
  • Consolidation of Lung Nodule detection and segmentation algorithms into pipelines.
  • Iterative parameter-space testing of pipelines in cluster-computing environments.
  • Perform evaluations against expert-contoured segmentations.
  • Explore and optimize existing Lung Nodule detection/segmentation tools.
  • Configure and launch StarCluster Nodes on AWS to run pipelines.
  • Use LIDC Data for evaluation of pipelines (~1200+ CT Images with manual contours).
  • Evaluate Segmentation pipelines against manual contours using metrics such as SimpleITK filters, Dice, Hausdorff, and Radiomics.

References

Slides describing initial experiments

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