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Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation
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10.1118/1.3284530
/content/aapm/journal/medphys/37/2/10.1118/1.3284530
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/37/2/10.1118/1.3284530

Figures

Image of FIG. 1.
FIG. 1.

A schematic of the construction of the normalized probabilistic atlases of liver and spleen.

Image of FIG. 2.
FIG. 2.

A schematic of the automated liver and spleen segmentation algorithm.

Image of FIG. 3.
FIG. 3.

Normalized probabilistic atlases of the liver (a) and spleen (b) were created using a modified affine transformation: (i) Image of two organs before registration, (ii) after the modified affine registration; and (iii) the probabilistic atlas with a probability color map. Each atlas voxel contains probabilities associated with the presence of the liver or spleen.

Image of FIG. 4.
FIG. 4.

An example of automated organ segmentation, liver, and spleen: (a) The patient image ; (b) the smoothed data ; (c) the conservative model of organs overlaid on the patient data; (d) the mean model of organs overlaid on the patient data; (e) the registered conservative model after the global affine registration covering the patient liver/spleen; (f) the registered mean model after the global affine registration; (g) the mean model after nonlinear registration ; (h) the segmentation after GAC and adaptive convolution ; and (i) the final segmentation after shape and location corrections .

Image of FIG. 5.
FIG. 5.

Bland–Altman agreement plots for the linear estimations of liver height at MHL; from left to right we show the interobserver variability and the difference between manual (observers 1 and 2) and automatic (CAD) measurements.

Image of FIG. 6.
FIG. 6.

Bland–Altman agreement plots for the linear estimations of spleen CC height; from left to right we show the interobserver variability and the difference between manual (observers 1 and 2) and automatic (CAD) measurements. The discrete 5 mm spaced steps are related to the slice thickness of image data.

Image of FIG. 7.
FIG. 7.

An example of liver and spleen automatically segmented from a normal test case. 2D axial slices of the 3D CT data are shown.

Image of FIG. 8.
FIG. 8.

Volume renderings of the segmentation of liver and spleen; (a) is a posterior view and (b) an anterior view. The liver and spleen ground truths are shown in dark colors with automated segmentation errors overlaid in light shades.

Image of FIG. 9.
FIG. 9.

Three examples of segmentations of pathological, enlarged livers with unusual shapes from three different patients.

Image of FIG. 10.
FIG. 10.

Examples from three different patients of segmentations of abnormal, enlarged spleens.

Image of FIG. 11.
FIG. 11.

An example of pathological liver segmentation from the MICCAI data. 2D axial slices of the 3D CT data are shown.

Image of FIG. 12.
FIG. 12.

Three examples of segmentation of livers from cases with partial hepatectomy from three different patients.

Tables

Generic image for table
TABLE I.

Statistics for the liver segmentation results from training and test data. Incremental results are shown for the training set at step 1—after nonrigid registration, step 2—using GAC and adaptive convolution, and step 3—after incorporating shape and location correction. Columns present the DC, VO, VER, HER, RMSE, and ASD.

Generic image for table
TABLE II.

Comparative statistics ( values) between different steps in the liver segmentation from the training set and between test sets of low and high resolutions (see Table I).

Generic image for table
TABLE III.

Statistics for the spleen segmentation results from training and test data. Incremental results are shown for the training set at step 1—after nonrigid registration, step 2—using GAC and adaptive convolution, and step 3—after incorporating shape and location correction. Columns present the DC, VO, VER, HER, RMSE, and ASD.

Generic image for table
TABLE IV.

Comparative statistics ( values) between different steps in the spleen segmentation from the training set and between test sets of low and high resolution (see Table III).

Generic image for table
TABLE V.

Comparative results for the liver segmentation (in alphabetical order of authors). Columns present the DC, VO, VER, RMSE, and ASD.

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/content/aapm/journal/medphys/37/2/10.1118/1.3284530
2010-01-25
2014-04-23
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/37/2/10.1118/1.3284530
10.1118/1.3284530
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