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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
Examples of automatic segmentation results for selected subjects: (a) cervical spine, (b) respiratory tract, (c) mandible, (d) hyoid, (e) thyroid cartilage, (f) jugular vein, (g) common carotid artery, and (h) sternocleidomastoid muscle.
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Figure 3: Examples of automatic segmentation results for selected subjects: (a) cervical spine, (b) respiratory tract, (c) mandible, (d) hyoid, (e) thyroid cartilage, (f) jugular vein, (g) common carotid artery, and (h) sternocleidomastoid muscle.

Mentions: The anatomical structures of interests are segmented in the order according to the reliability of successful detection. In addition to domain knowledge of gray tone range, size and shape, each structure is also associated with location relative to other structures that can be found prior to itself. For each structure, we first run the dynamic thresholding process to find 2D regions in axial slices according to domain knowledge. Note that some axial slices may yield successful 2D segmentation results and others do not, we then the 3D active contouring to build a 3D model using the partial 2D segmentation result as the initial seed. Also note that the active contouring may not grow the structure perfectly to its full extent, but sufficient to provide landmark information. This two step process is then repeated for the next structure. 3D surface meshes of those structures are constructed for both the reference and target subjects. The meshes can be used as landmarks to improve the alignment and to measure similarity between the subjects. Figure 3 shows examples of segmentation results on selected subjects.


Head and neck lymph node region delineation with image registration.

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

Examples of automatic segmentation results for selected subjects: (a) cervical spine, (b) respiratory tract, (c) mandible, (d) hyoid, (e) thyroid cartilage, (f) jugular vein, (g) common carotid artery, and (h) sternocleidomastoid muscle.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Examples of automatic segmentation results for selected subjects: (a) cervical spine, (b) respiratory tract, (c) mandible, (d) hyoid, (e) thyroid cartilage, (f) jugular vein, (g) common carotid artery, and (h) sternocleidomastoid muscle.
Mentions: The anatomical structures of interests are segmented in the order according to the reliability of successful detection. In addition to domain knowledge of gray tone range, size and shape, each structure is also associated with location relative to other structures that can be found prior to itself. For each structure, we first run the dynamic thresholding process to find 2D regions in axial slices according to domain knowledge. Note that some axial slices may yield successful 2D segmentation results and others do not, we then the 3D active contouring to build a 3D model using the partial 2D segmentation result as the initial seed. Also note that the active contouring may not grow the structure perfectly to its full extent, but sufficient to provide landmark information. This two step process is then repeated for the next structure. 3D surface meshes of those structures are constructed for both the reference and target subjects. The meshes can be used as landmarks to improve the alignment and to measure similarity between the subjects. Figure 3 shows examples of segmentation results on selected subjects.

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