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Segmentation of liver, its vessels and lesions from CT images for surgical planning.

Oliveira DA, Feitosa RQ, Correia MM - Biomed Eng Online (2011)

Bottom Line: It attained visual consistent results for nodules and veins segmentation, and we compiled the results, showing the best, worst, and mean results for all dataset.The method for liver segmentation performed well, according to the results of the numerical evaluation implemented, and the segmentation of liver internal structures were consistent with the anatomy of the liver, as confirmed by a specialist.The analysis provided evidences that the method to segment the liver may be applied to segment other organs, especially to those whose distribution of voxel intensities is nearly Gaussian shaped.

View Article: PubMed Central - HTML - PubMed

Affiliation: Electrical Engineering Department, Pontifical Catholic University of Rio de Janeiro, Marquês de São Vicente, Brasil. dario@ele.puc-rio.br

ABSTRACT

Background: Cancer treatments are complex and involve different actions, which include many times a surgical procedure. Medical imaging provides important information for surgical planning, and it usually demands a proper segmentation, i.e., the identification of meaningful objects, such as organs and lesions. This study proposes a methodology to segment the liver, its vessels and nodules from computer tomography images for surgical planning.

Methods: The proposed methodology consists of four steps executed sequentially: segmentation of liver, segmentation of vessels and nodules, identification of hepatic and portal veins, and segmentation of Couinaud anatomical segments. Firstly, the liver is segmented by a method based on a deformable model implemented through level sets, of which parameters are adjusted by using a supervised optimization procedure. Secondly, a mixture model is used to segment nodules and vessels through a region growing process. Then, the identification of hepatic and portal veins is performed using liver anatomical knowledge and a vein tracking algorithm. Finally, the Couinaud anatomical segments are identified according to the anatomical liver model proposed by Couinaud.

Results: Experiments were conducted using data and metrics brought from the liver segmentation competition held in the Sliver07 conference. A subset of five exams was used for estimation of segmentation parameter values, while 15 exams were used for evaluation. The method attained a good performance in 17 of the 20 exams, being ranked as the 6th best semi-automatic method when comparing to the methods described on the Sliver07 website (2008). It attained visual consistent results for nodules and veins segmentation, and we compiled the results, showing the best, worst, and mean results for all dataset.

Conclusions: The method for liver segmentation performed well, according to the results of the numerical evaluation implemented, and the segmentation of liver internal structures were consistent with the anatomy of the liver, as confirmed by a specialist. The analysis provided evidences that the method to segment the liver may be applied to segment other organs, especially to those whose distribution of voxel intensities is nearly Gaussian shaped.

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Vessels and nodules segmentation: axial, coronal, sagittal and 3D views. The liver appears in light green, the vessels in red and the nodules in dark green.
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Related In: Results  -  Collection

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Figure 11: Vessels and nodules segmentation: axial, coronal, sagittal and 3D views. The liver appears in light green, the vessels in red and the nodules in dark green.

Mentions: The evaluation of nodules and vessels segmentation was visual, since no reference data is available. Figure 11 shows examples of the outcome. The results are visually consistent, with brighter regions classified as vessels, and darker as nodules.


Segmentation of liver, its vessels and lesions from CT images for surgical planning.

Oliveira DA, Feitosa RQ, Correia MM - Biomed Eng Online (2011)

Vessels and nodules segmentation: axial, coronal, sagittal and 3D views. The liver appears in light green, the vessels in red and the nodules in dark green.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3094217&req=5

Figure 11: Vessels and nodules segmentation: axial, coronal, sagittal and 3D views. The liver appears in light green, the vessels in red and the nodules in dark green.
Mentions: The evaluation of nodules and vessels segmentation was visual, since no reference data is available. Figure 11 shows examples of the outcome. The results are visually consistent, with brighter regions classified as vessels, and darker as nodules.

Bottom Line: It attained visual consistent results for nodules and veins segmentation, and we compiled the results, showing the best, worst, and mean results for all dataset.The method for liver segmentation performed well, according to the results of the numerical evaluation implemented, and the segmentation of liver internal structures were consistent with the anatomy of the liver, as confirmed by a specialist.The analysis provided evidences that the method to segment the liver may be applied to segment other organs, especially to those whose distribution of voxel intensities is nearly Gaussian shaped.

View Article: PubMed Central - HTML - PubMed

Affiliation: Electrical Engineering Department, Pontifical Catholic University of Rio de Janeiro, Marquês de São Vicente, Brasil. dario@ele.puc-rio.br

ABSTRACT

Background: Cancer treatments are complex and involve different actions, which include many times a surgical procedure. Medical imaging provides important information for surgical planning, and it usually demands a proper segmentation, i.e., the identification of meaningful objects, such as organs and lesions. This study proposes a methodology to segment the liver, its vessels and nodules from computer tomography images for surgical planning.

Methods: The proposed methodology consists of four steps executed sequentially: segmentation of liver, segmentation of vessels and nodules, identification of hepatic and portal veins, and segmentation of Couinaud anatomical segments. Firstly, the liver is segmented by a method based on a deformable model implemented through level sets, of which parameters are adjusted by using a supervised optimization procedure. Secondly, a mixture model is used to segment nodules and vessels through a region growing process. Then, the identification of hepatic and portal veins is performed using liver anatomical knowledge and a vein tracking algorithm. Finally, the Couinaud anatomical segments are identified according to the anatomical liver model proposed by Couinaud.

Results: Experiments were conducted using data and metrics brought from the liver segmentation competition held in the Sliver07 conference. A subset of five exams was used for estimation of segmentation parameter values, while 15 exams were used for evaluation. The method attained a good performance in 17 of the 20 exams, being ranked as the 6th best semi-automatic method when comparing to the methods described on the Sliver07 website (2008). It attained visual consistent results for nodules and veins segmentation, and we compiled the results, showing the best, worst, and mean results for all dataset.

Conclusions: The method for liver segmentation performed well, according to the results of the numerical evaluation implemented, and the segmentation of liver internal structures were consistent with the anatomy of the liver, as confirmed by a specialist. The analysis provided evidences that the method to segment the liver may be applied to segment other organs, especially to those whose distribution of voxel intensities is nearly Gaussian shaped.

Show MeSH