Medical image computing and computer-assisted interventionAutomated Segmentation of the Liver from 3D CT Images Using Probabilistic Atlas and Multilevel Statistical Shape Model
Section snippets
Spatial Normalization Using the Abdominal Cavity
Given training datasets, PA and ML-SSM are constructed. Before construction, spatial normalization of datasets is necessary. To achieve this, one CT dataset that was judged to display average liver shape by a radiology specialist was selected as a standard patient, and the abdominal cavity was regarded as the normalized space to represent normalized liver position and shape (12). A rough abdominal cavity is used in this work, because extracting the precise region of the abdominal cavity is not
Experimental Conditions
A total of 28 abdominal CT datasets (slice thickness 2.5 mm, pitch 1.25 mm, field of view 350 × 350 mm2, 512 × 512 matrix, 159 slices) were used. We randomly selected eight datasets for evaluation, with the others used for training. The PA and ML-SSM were constructed from the 20 training datasets. A radiology specialist judged the livers as highly deformed from disease in nine of the 20 training datasets. Livers were also highly deformed in five of the eight evaluation datasets.
In the nonrigid
Discussion
In cases without the adhesiveness constraint, when the hierarchy of ML-SSM changed from level 2 to level 3, segmentation accuracy decreased (as shown in Table 1) and large inconsistencies among patches of the estimated shape occurred. When the hierarchy of ML-SSM changed from level 1 to level 2, large inconsistencies occurred although segmentation accuracy increased. For large λ (λ ≥ 0.05), segmentation accuracy was improved as the hierarchy level of ML-SSM increased, and inconsistency could be
Conclusion
We have developed an automated segmentation method of the liver using statistical atlases. The proposed method was shown to improve segmentation accuracy for datasets including highly deformed livers by combining initial segmentation based on PA and subsequent ML-SSM fitting. We also demonstrate that the adhesiveness constraint was effective for dealing with inconsistencies at finer levels of ML-SSM. Accuracy was degraded at the fine level (level 3) without use of the constraint. We are now
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