A conditional statistical shape model with integrated error estimation of the conditions; Application to liver segmentation in non-contrast CT images
A novel relaxed conditional SSM with integrated error estimation is created. The conditional SSM is flexible and data-driven. The model can bridge between non-conditional and conventional conditional SSMs. The proposed method outperforms state of the art algorithms in liver segmentation. Improved performance is mainly in difficult to segment and oddly shaped cases.
conditional statistical shape model liver segmentation non-contrast ct volume level set