Assistance in Destroying Focal Lesions in the Liver Using Image Navigation

Spińczyk, Dominik

doi:10.1007/978-3-319-06596-0_2

Cited by 2 publications

(1 citation statement)

References 11 publications

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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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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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Towards the clinical integration of an image-guided navigation system for percutaneous liver tumor ablation using freehand 2D ultrasound images

Spińczyk

2015

Computer Aided Surgery

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Primary and metastatic liver tumors constitute a significant challenge for contemporary medicine. Several improvements are currently being developed and implemented to advance image navigation systems for percutaneous liver focal lesion ablation in clinical applications at the diagnosis, planning and intervention stages. First, the automatic generation of an anatomically accurate parametric model of the preoperative patient liver was proposed in addition to a method to visually evaluate and make manual corrections. Second, a marker was designed to facilitate rigid registration between the model of the preoperative patient liver and the patient during treatment. A specific approach was implemented and tested for rigid mapping by continuously tracking a set of uniquely identified markers and by accounting for breathing motion, facilitating the determination of the optimal breathing phase for needle insertion into the liver tissue. Third, to overcome the challenge of tracking the absolute position of the planned target point, an intra-operative ultrasound (US) system was integrated based on the Public Software Library for UltraSound and OpenIGTLink protocol, which tracks breathing motion in a 2D time sequence of US images. Additionally, to improve the visibility of liver focal lesions, an approach to determine spatio-temporal correspondence between the US sequence and the 4D computed tomography (CT) examination was developed, implemented and tested. This proposed method of processing anatomical model, rigid registration approach and the implemented US tracking and fusion method were tested in 20 anonymized CT and in 10 clinical cases, respectively. The presented methodology can be applied and used with any older 2D US systems, which are currently commonly used in clinical practice.

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Assistance in Destroying Focal Lesions in the Liver Using Image Navigation

Cited by 2 publications

References 11 publications

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

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

Towards the clinical integration of an image-guided navigation system for percutaneous liver tumor ablation using freehand 2D ultrasound images

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