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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.

Show MeSH
Comparison of results from the image registration methods with and without using landmark correspondence. Rows A-E show selected axial CT slices in the neighborhood of the hyoid in various data sets from superior to inferior. Column 1 shows slices from the reference subject, column 4 from the target subject, column 2 is the result of Mattes' image registration method, and column 3 is the result of the new method using landmark correspondence.
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Figure 5: Comparison of results from the image registration methods with and without using landmark correspondence. Rows A-E show selected axial CT slices in the neighborhood of the hyoid in various data sets from superior to inferior. Column 1 shows slices from the reference subject, column 4 from the target subject, column 2 is the result of Mattes' image registration method, and column 3 is the result of the new method using landmark correspondence.

Mentions: Figure 5 shows an example of qualitative comparison of image registration results using the Mattes' method without landmark information and the proposed method which incorporates landmark correspondence. Rows A-E show selected axial CT slices in the neighborhood of the hyoid in various data sets from superior to inferior location. Column 1 shows slices from the reference subject, and column 4 shows slices from the target subject. Columns 2 and 3 show transformed reference images which were re-sampled to match or align with the target images using the transformation function g(x/μ) of equation (1) produced from the image registration procedure. Column 2 is the result of Mattes' image registration method without using landmark information, and column 3 is the result of the proposed method using landmark correspondence. The images in column 3 match better qualitatively to column 4 in several ways. First of all, for example, the outer body contours is closer to the ones of target CT. Second, the superior-inferior location of the hyoid bone and the mandible in column 3 match more closely to the ones in column 4; such as the hyoid bone is in slices (rows) C-E (and beyond) of column 2, but in slices A¬D of column 4, and the inferior boundary of the mandible ends in slice D of column 2, but continues beyond slice E in columns 3 and 4. This is important because both hyoid and mandible represent superior-inferior boundaries to adjacent lymph node levels. Third, the proximity of the cervical spine in column 3 appears to be more closely matched to column 4. Even though certain soft tissue structures appear distorted in column 3; in the context of delineating lymph node regions, it is relatively less important compared to matching the location of the dominant structures defining the boundaries.


Head and neck lymph node region delineation with image registration.

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

Comparison of results from the image registration methods with and without using landmark correspondence. Rows A-E show selected axial CT slices in the neighborhood of the hyoid in various data sets from superior to inferior. Column 1 shows slices from the reference subject, column 4 from the target subject, column 2 is the result of Mattes' image registration method, and column 3 is the result of the new method using landmark correspondence.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Comparison of results from the image registration methods with and without using landmark correspondence. Rows A-E show selected axial CT slices in the neighborhood of the hyoid in various data sets from superior to inferior. Column 1 shows slices from the reference subject, column 4 from the target subject, column 2 is the result of Mattes' image registration method, and column 3 is the result of the new method using landmark correspondence.
Mentions: Figure 5 shows an example of qualitative comparison of image registration results using the Mattes' method without landmark information and the proposed method which incorporates landmark correspondence. Rows A-E show selected axial CT slices in the neighborhood of the hyoid in various data sets from superior to inferior location. Column 1 shows slices from the reference subject, and column 4 shows slices from the target subject. Columns 2 and 3 show transformed reference images which were re-sampled to match or align with the target images using the transformation function g(x/μ) of equation (1) produced from the image registration procedure. Column 2 is the result of Mattes' image registration method without using landmark information, and column 3 is the result of the proposed method using landmark correspondence. The images in column 3 match better qualitatively to column 4 in several ways. First of all, for example, the outer body contours is closer to the ones of target CT. Second, the superior-inferior location of the hyoid bone and the mandible in column 3 match more closely to the ones in column 4; such as the hyoid bone is in slices (rows) C-E (and beyond) of column 2, but in slices A¬D of column 4, and the inferior boundary of the mandible ends in slice D of column 2, but continues beyond slice E in columns 3 and 4. This is important because both hyoid and mandible represent superior-inferior boundaries to adjacent lymph node levels. Third, the proximity of the cervical spine in column 3 appears to be more closely matched to column 4. Even though certain soft tissue structures appear distorted in column 3; in the context of delineating lymph node regions, it is relatively less important compared to matching the location of the dominant structures defining the boundaries.

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