There is a wide range of segmentation methods for bone structures in CT images. Many of these methods are declared as automatic, but it is not guaranteed, that the resulting segmentation labels the volume of interest correctly in any case. This work presents a technique, which assists the user with the necessary corrections of the segmentation errors. The procedure must be started manually, but the following steps are fully automatic. First, a similar, correct segmentation is selected from a database, which is used to mask the defects. Then the selected segmentation is registered onto the defect one using the diffeomorphic demons algorithm. Thereby, the region inside the mask is excluded from registration but the displacement field is interpolated. The method has been implemented and tested for segmentations of the proximal femur head, but can easily be transferred to segmentations of other bone regions.
Keywords
- Proximal Femur
- Binary Image
- Segmentation Error
- Rigid Registration
- Statistical Shape Model
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.