Target Registration Error minimization involving deformable organs using elastic body splines and Particle Swarm Optimization approach

Spińczyk, Dominik; Fabian, Sylwester

doi:10.1016/j.suronc.2017.09.005

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…The AQUIRC algorithm uses multiple strings to modify the marker positions with FLE estimation and then registers between the modified marker positions. Spinczyk et al [ 34 ] achieved a significantly lower median TRE based on deformation made using spline curves and a set of successive marker positions on the surface. They assessed the correlation between FRE and TRE and compared the TRE obtained with different registration methods.…”

Section: Methodsmentioning

confidence: 99%

Modeling of Respiratory Motion to Support the Minimally Invasive Destruction of Liver Tumors

Spińczyk

Fabian

Król

2022

Sensors

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Objective: Respiratory movements are a significant factor that may hinder the use of image navigation systems during minimally invasive procedures used to destroy focal lesions in the liver. This article aims to present a method of estimating the displacement of the target point due to respiratory movements during the procedure, working in real time. Method: The real-time method using skin markers and non-rigid registration algorithms has been implemented and tested for various classes of transformation. The method was validated using clinical data from 21 patients diagnosed with liver tumors. For each patient, each marker was treated as a target and the remaining markers as target position predictors, resulting in 162 configurations and 1095 respiratory cycles analyzed. In addition, the possibility of estimating the respiratory phase signal directly from intraoperative US images and the possibility of synchronization with the 4D CT respiratory sequence are also presented, based on ten patients. Results: The median value of the target registration error (TRE) was 3.47 for the non-rigid registration method using the combination of rigid transformation and elastic body spline curves, and an adaptation of the assessing quality using image registration circuits (AQUIRC) method. The average maximum distance was 3.4 (minimum: 1.6, maximum 6.8) mm. Conclusions: The proposed method obtained promising real-time TRE values. It also allowed for the estimation of the TRE at a given geometric margin level to determine the estimated target position. Directions for further quantitative research and the practical possibility of combining both methods are also presented.

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Section: Methodsmentioning

confidence: 99%

Modeling of Respiratory Motion to Support the Minimally Invasive Destruction of Liver Tumors

Spińczyk

Fabian

Król

2022

Sensors

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“…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 ].…”

Section: Introductionmentioning

confidence: 99%

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

Spińczyk

Krasoń

2018

BioMed Eng OnLine

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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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Preliminary Study of Modeling Sagging Breasts for Support Navigation in Ultrasound Guided Biopsy

Juraszczyk

Spińczyk

2018

Advances in Intelligent Systems and Computing

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Target Registration Error minimization involving deformable organs using elastic body splines and Particle Swarm Optimization approach

Cited by 4 publications

References 15 publications

Modeling of Respiratory Motion to Support the Minimally Invasive Destruction of Liver Tumors

Modeling of Respiratory Motion to Support the Minimally Invasive Destruction of Liver Tumors

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

Preliminary Study of Modeling Sagging Breasts for Support Navigation in Ultrasound Guided Biopsy

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