Automatic liver segmentation in computed tomography using general-purpose shape modeling methods

• Main Authors: Dominik Spinczyk, Agata Krasoń
• Format: Article
• Language: English
• Published: BMC 2018-05-01
• Series: BioMedical Engineering OnLine
• Subjects: Liver segmentation; Single atlas based segmentation; Active Shape Model; Gaussian Process Morphable Models
• Online Access: http://link.springer.com/article/10.1186/s12938-018-0504-6

Abstract Background Liver segmentation in computed tomography is required in many clinical applications. The segmentation methods used can be classified according to a number of criteria. One important criterion for method selection is the shape representation of the segmented organ. The aim of the work is automatic liver segmentation using general purpose shape modeling methods. Methods As part of the research, methods based on shape information at various levels of advancement were used. The single atlas based segmentation method was used as the simplest shape-based method. This method is derived from a single atlas using the deformable free-form deformation of the control point curves. Subsequently, the classic and modified Active Shape Model (ASM) was used, using medium body shape models. As the most advanced and main method generalized statistical shape models, Gaussian Process Morphable Models was used, which are based on multi-dimensional Gaussian distributions of the shape deformation field. Results Mutual information and sum os square distance were used as similarity measures. The poorest results were obtained for the single atlas method. For the ASM method in 10 analyzed cases for seven test images, the Dice coefficient was above 55$$\%$$ % , of which for three of them the coefficient was over 70$$\%$$ % , which placed the method in second place. The best results were obtained for the method of generalized statistical distribution of the deformation field. The DICE coefficient for this method was 88.5$$\%$$ % Conclusions This value of 88.5 $$\%$$ % Dice coefficient can be explained by the use of general-purpose shape modeling methods with a large variance of the shape of the modeled object—the liver and limitations on the size of our training data set, which was limited to 10 cases. The obtained results in presented fully automatic method are comparable with dedicated methods for liver segmentation. In addition, the deforamtion features of the model can be modeled mathematically by using various kernel functions, which allows to segment the liver on a comparable level using a smaller learning set.