“…Registration reffers to as a selection the set of the parameters: where —is the cost function related to the similarity metrics. The other important details can be found in a previous paper [ 5 ]. Fig.…”
Section: Methodsmentioning
confidence: 99%
“…Liver segmentation has been the subject of research by various authors for many years and is required in many clinical applications [ 1 – 3 ]. The most popular imaging mode is computed tomography (CT) or the contrast-enhanced CT, which are used for computer aided diagnosis (CAD) [ 4 , 5 ] and planning and support of computer assisted interventions (CAI) of primary and secondary tumors in liver [ 6 – 9 ]. Precise liver segmentation is also crucial for selective internal radiation therapy (SIRT) [ 10 ].…”
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.
“…Registration reffers to as a selection the set of the parameters: where —is the cost function related to the similarity metrics. The other important details can be found in a previous paper [ 5 ]. Fig.…”
Section: Methodsmentioning
confidence: 99%
“…Liver segmentation has been the subject of research by various authors for many years and is required in many clinical applications [ 1 – 3 ]. The most popular imaging mode is computed tomography (CT) or the contrast-enhanced CT, which are used for computer aided diagnosis (CAD) [ 4 , 5 ] and planning and support of computer assisted interventions (CAI) of primary and secondary tumors in liver [ 6 – 9 ]. Precise liver segmentation is also crucial for selective internal radiation therapy (SIRT) [ 10 ].…”
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.
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