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

Abstract

BackgroundLiver 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.MethodsAs 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.ResultsMutual 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 88.5ConclusionsThis 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.

Combining statistical shape modeling, CFD, and meta‐modeling to approximate the patient‐specific pressure‐drop across the aortic valve in real‐time

“…The large number of publications in various bio-medical fields proves that statistical shape modeling is a versatile and widely accepted technique to capture anatomic variation in the population. For example, it is extensively used for organ segmentation, [36][37][38] to extract morphological bio-markers of (diseased) organs, [39][40][41] or for numerous orthopedic applications. [42][43][44][45][46][47] Although few, several studies-mainly in the field of orthopedics-attempted to relate SSM parameterized anatomical shape variation with Finite Element Analysis (FEA).…”

Intensity Profiles in Active Shape Model

“…Spinczyk and Krasoń [26] authors proposed an automatic liver segmentation using general purpose shape modeling methods. The atlas based segmentation was used as being the simplest shape-based method.…”

Automatic Liver Segmentation in CT Images with Enhanced GAN and Mask Region-Based CNN Architectures

“…Firstly, the segmentation performance is typically degraded by the influence of complex surrounding blood vessels and organs [ 8 ]. Also, the liver shape shows high variability across different sections in the same set of CT images [ 9 , 10 ]. In addition, the density of the liver tissues is highly similar to the densities of many other types of soft tissues in the abdominal cavity [ 11 ].…”