An iterative Bayesian approach for nearly automatic liver segmentation: Algorithm and validation

Purpose: We present a new algorithm for nearly automatic liver segmentation and volume estimation from abdominal Computed Tomography Angiography (CTA) images and its validation. Materials and methods: Our hybrid algorithm uses a multiresolution iterative scheme. It starts from a single user-defined pixel seed inside the liver, and repeatedly applies smoothed Bayesian classification to identify the liver and other organs, followed by adaptive morphological operations and active contours refinement. We evaluate the algorithm with two retrospective studies on 56 validated CTA images. The first study compares it to ground-truth manual segmentation and semi-automatic and automatic commercial methods. The second study uses the public data-set SLIVER07 and its comparison methodology. Results: We achieved for both studies, correlations of 0.98 and 0.99 for liver volume estimation, with mean volume differences of 5.36 and 2.68% with respect to manual ground-truth estimation, and mean volume variability for different initial seeds of 0.54 and 0.004%, respectively. For the second study, our algorithm scored 71.8 and 67.87 for the training and test datasets, which compares very favorably with other semi-automatic methods. Conclusions: Our algorithm requires minimal interaction by a non-expert user, is accurate, efficient, and robust to initial seed selection. It can be effective for hepatic volume estimation and liver modeling in a clinical setup.