Difference between revisions of "Projects:MGH-HeadAndNeck-PtSetReg"

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= Description =
 
= Description =
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In this work, interactive segmentation is integrated with an active contour model and segmentation is posed as a human-supervisory control problem. User input is tightly coupled with an automatic segmentation algorithm leveraging the user's high-level anatomical knowledge and the automated method's speed. Real-time visualization enables the user to quickly identify and correct the result in a sub-domain where the variational model's statistical assumptions do not agree with his expert knowledge. Methods developed in this work are applied to magnetic resonance imaging (MRI) volumes as part of a population study of human skeletal development. Segmentation time is reduced by approximately five times over similarly accurate manual segmentation of large bone structures.
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In this example, large misalignment is present between the two patients.
  
  
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* [[Image:KSliceFlowChart.png | Rel Pred| 800px]]
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Point clouds are generated from label maps of bone. The computed registration field, which is guaranteed to be injective, is applied to the original CT volumes.
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* [[Image:KSliceFlowChart.png | PreRegFleshSkeleton| 800px]]
 
Flowchart for the interactive segmentation approach. Notice the user's pivotal role in the process.
 
Flowchart for the interactive segmentation approach. Notice the user's pivotal role in the process.
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* [[Image:KSliceInptTimeChart.png | Eye Seg| 800px]]
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Time-line of user input into the system. Note that user input is sparse, has local effect only, and decreases in frequency and magnitude over time.
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* [[Image:KVoutSegTightMod.png | Eye Seg| 300px]]
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Another set of point clouds is generated by sampling from label maps of flesh. To avoid undoing the previous registration, regions belonging to the registered bone tissue from above are constrained not to move. Another injective deformation field is computed.  
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Result of the segmentation.
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The result of
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== Current State of Work ==
 
== Current State of Work ==

Revision as of 03:02, 19 November 2012

Home < Projects:MGH-HeadAndNeck-PtSetReg
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Semi-Automatic Image Registration

We recognize that the difference between a failure of an automatic image registration approach and a success of a semi-automatic method can be a small amount of user input. The goal of this work is to register two CT volumes of different patients that are related by a large deformation. The user sets two thresholds for each image: one for the bone mask and another for flesh tissue. This operation is not time consuming but simplifies the registration task dramatically for the automatic algorithm.

Description

In this example, large misalignment is present between the two patients.


Point clouds are generated from label maps of bone. The computed registration field, which is guaranteed to be injective, is applied to the original CT volumes.

  • PreRegFleshSkeleton

Flowchart for the interactive segmentation approach. Notice the user's pivotal role in the process.


Another set of point clouds is generated by sampling from label maps of flesh. To avoid undoing the previous registration, regions belonging to the registered bone tissue from above are constrained not to move. Another injective deformation field is computed.


The result of


Current State of Work

A pipeline composed of Matlab and mex-ed C++ code has been implemented.

Key Investigators

  • Georgia Tech: Ivan Kolesov, Patricio Vela
  • Boston University: Jehoon Lee, Allen Tannenbaum
  • MGH: Gregory Sharp

Publications

In Press

I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.

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