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Head and neck lymph node region delineation with image registration.

Teng CC, Shapiro LG, Kalet IJ - Biomed Eng Online (2010)

Bottom Line: We are also proposing a method that could help us identify the reference models which could potentially produce the best results.The computer generated lymph node regions are evaluated quantitatively and qualitatively.Although not conforming to clinical criteria, the results suggest the technique has promise.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Technology, Brigham Young University, Provo, UT, USA. ccteng@byu.edu

ABSTRACT

Background: The success of radiation therapy depends critically on accurately delineating the target volume, which is the region of known or suspected disease in a patient. Methods that can compute a contour set defining a target volume on a set of patient images will contribute greatly to the success of radiation therapy and dramatically reduce the workload of radiation oncologists, who currently draw the target by hand on the images using simple computer drawing tools. The most challenging part of this process is to estimate where there is microscopic spread of disease.

Methods: Given a set of reference CT images with "gold standard" lymph node regions drawn by the experts, we are proposing an image registration based method that could automatically contour the cervical lymph code levels for patients receiving radiation therapy. We are also proposing a method that could help us identify the reference models which could potentially produce the best results.

Results: The computer generated lymph node regions are evaluated quantitatively and qualitatively.

Conclusions: Although not conforming to clinical criteria, the results suggest the technique has promise.

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Related in: MedlinePlus

Hausdorff and mean distance (in cm) between transformed reference mesh and the target mesh of nodal regions for all SR and ST, comparing image registration results from Mattes method and the proposed landmark method.
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Figure 6: Hausdorff and mean distance (in cm) between transformed reference mesh and the target mesh of nodal regions for all SR and ST, comparing image registration results from Mattes method and the proposed landmark method.

Mentions: Figure 6 shows comparisons of image registration results from the Mattes method and the proposed method incorporating the landmark correspondence. The horizontal axis represents the Hausdorff or mean distance between projected lymph node regions based on the reference model using image registration results from the Mattes method and corresponding expert drawn lymph node regions of the target subject. The vertical axis represents matching results using the proposed registration method. The diagonal dotted lines represent the points where the two measures are equal. The figures show overall improvement using the proposed registration method. Tables 4 and 5 compare the mean and standard deviation of results from two methods. The new landmark-based method improved the average Hausdorff distance by as much as 25%, and the average mean distance by as much as 42%.


Head and neck lymph node region delineation with image registration.

Teng CC, Shapiro LG, Kalet IJ - Biomed Eng Online (2010)

Hausdorff and mean distance (in cm) between transformed reference mesh and the target mesh of nodal regions for all SR and ST, comparing image registration results from Mattes method and the proposed landmark method.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2902481&req=5

Figure 6: Hausdorff and mean distance (in cm) between transformed reference mesh and the target mesh of nodal regions for all SR and ST, comparing image registration results from Mattes method and the proposed landmark method.
Mentions: Figure 6 shows comparisons of image registration results from the Mattes method and the proposed method incorporating the landmark correspondence. The horizontal axis represents the Hausdorff or mean distance between projected lymph node regions based on the reference model using image registration results from the Mattes method and corresponding expert drawn lymph node regions of the target subject. The vertical axis represents matching results using the proposed registration method. The diagonal dotted lines represent the points where the two measures are equal. The figures show overall improvement using the proposed registration method. Tables 4 and 5 compare the mean and standard deviation of results from two methods. The new landmark-based method improved the average Hausdorff distance by as much as 25%, and the average mean distance by as much as 42%.

Bottom Line: We are also proposing a method that could help us identify the reference models which could potentially produce the best results.The computer generated lymph node regions are evaluated quantitatively and qualitatively.Although not conforming to clinical criteria, the results suggest the technique has promise.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Technology, Brigham Young University, Provo, UT, USA. ccteng@byu.edu

ABSTRACT

Background: The success of radiation therapy depends critically on accurately delineating the target volume, which is the region of known or suspected disease in a patient. Methods that can compute a contour set defining a target volume on a set of patient images will contribute greatly to the success of radiation therapy and dramatically reduce the workload of radiation oncologists, who currently draw the target by hand on the images using simple computer drawing tools. The most challenging part of this process is to estimate where there is microscopic spread of disease.

Methods: Given a set of reference CT images with "gold standard" lymph node regions drawn by the experts, we are proposing an image registration based method that could automatically contour the cervical lymph code levels for patients receiving radiation therapy. We are also proposing a method that could help us identify the reference models which could potentially produce the best results.

Results: The computer generated lymph node regions are evaluated quantitatively and qualitatively.

Conclusions: Although not conforming to clinical criteria, the results suggest the technique has promise.

Show MeSH
Related in: MedlinePlus