A flexible algorithm for construction of 3-D vessel networks for use in thermal modeling

A new algorithm for the construction of artificial blood vessel networks is presented. The algorithm produces three-dimensional (3-D) geometrical representations of both arterial and venous networks. The key ingredient of the algorithm is a 3-D potential function defined in the tissue volume. This potential function controls the paths by which points are connected to existing vessels, thereby producing new vessel segments. The potential function has no physiological interpretation, but, by adjustment of parameters governing the potential, it is possible to produce networks that have physiologically meaningful geometrical properties. If desired, the veins can be generated counter current to the arteries. Furthermore, the potential function allows fashioning of the networks to the presence of bone or air cavities. The resulting networks can be used for thermal simulations of hyperthermia treatment

Registration of organs with sliding interfaces and changing topologies

Smoothness and continuity assumptions on the deformation field in deformable image registration do not hold for applications where the imaged objects have sliding interfaces. Recent extensions to deformable image registration that accommodate for sliding motion of organs are limited to sliding motion along approximately planar surfaces or cannot model sliding that changes the topological configuration in case of multiple organs. We propose a new extension to free-form image registration that is not limited in this way. Our method uses a transformation model that consists of uniform B-spline transformations for each organ region separately, which is based on segmentation of one image. Since this model can create overlapping regions or gaps between regions, we introduce a penalty term that minimizes this undesired effect. The penalty term acts on the surfaces of the organ regions and is optimized simultaneously with the image similarity. To evaluate our method registrations were performed on publicly available inhale-exhale CT scans for which performances of other methods are known. Target registration errors are computed on dense landmark sets that are available with these datasets. On these data our method outperforms the other methods in terms of target registration error and, where applicable, also in terms of overlap and gap volumes. The approximation of the other methods of sliding motion along planar surfaces is reasonably well suited for the motion present in the lung data. The ability of our method to handle sliding along curved boundaries and for changing region topology configurations was demonstrated on synthetic images. © 2014 SPIE

Automatic estimation of registration parameters : image similarity and regularization

Image registration is a procedure to spatially align two images that is often used in, for example, computer-aided diagnosis or segmentation applications. To maximize the flexibility of image registration methods, they depend on many registration parameters that must be fine-tuned for each specific application. Tuning parameters is a time-consuming task, that would ideally be performed for each individual registration. However, doing this manually for each registration is too time-consuming, and therefore we would like to do this automatically. This paper proposes a methodology to estimate one of most important parameters in a registration procedure, the regularization setting, on the basis of the image similarity. We test our method on a set of images of prostate cancer patients and show that using the proposed methodology, we can improve the result of image registration when compared to using an average-best parameter. © 2010 Copyright SPIE - The International Society for Optical Engineering

Tests of the geometrical description of blood vessels in a thermal model using counter-current geometries

We have developed a thermal model, for use in hyperthermia treatment planning, in which blood vessels are described as geometrical objects; 3D curves with associated diameters. For the calculation of the heat exchange with the tissue an analytic result is used. To arrive at this result some assumptions were made. One of these assumptions is a cylindrically symmetric temperature distribution. In this paper the behaviour of the model is examined for counter-current vessel geometries for which this assumption is not valid. Counter-current vessel pairs intersecting a circular tissue slice are tested. For these 2D geometries vessel spacing, tissue radius and resolution are varied, as well as the position of the vessel pair with respect to the discretized tissue grid. The simulation results are evaluated by comparison of the different heat flow rates with analytical predictions. The tests show that for a fixed vessel configuration the accuracy is not a simple decreasing function of the voxel dimensions, but is also sensitive to the position of the configuration with respect to the discretized tissue grid

Temperature simulations in tissue with a realistic computer generated vessel network

Abstract. The practical use of a discrete vessel thermal model for hyperthermia treatment planning requires a number of choices with respect to the unknown part of the patient's vasculature. This work presents a study of the thermal effects of blood flow in a simple tissue geometry with a detailed artificial vessel network. The simulations presented here demonstrate that an incomplete discrete description of the detailed network results in a better prediction of the temperature distribution than is obtained using the conventional bio-heatsink equation. Therefore, efforts to obtain information on the positions of the large vessels in an individual hyperthermia patient will be rewarded with a more accurate prediction of the temperature distribution

Free-form registration involving disappearing structures : application to brachytherapy MRI

Registration of two images is difficult if large deformations are induced due to the absence of a structure in one image. We propose a penalty term that minimizes the volume of the missing structure in one image during free-form registration. The registration optimum found is based on image similarity, provided that the missing volume is minimal. We demonstrate our method on cervical MR images for brachytherapy. The intrapatient registration problem involves one image in which a therapy applicator is present and one in which it is not. Experiments show improvement of registration when including the penalty term. The improvements of surface distance and overlap of the bladder and rectum (which are close to the applicator volume) provide proof of principle of our method. © 2013 Springer-Verlag

Free-form image registration regularized by a statistical shape model : application to organ segmentation in cervical MR

Deformable registration is prone to errors when it involves large and complex deformations, since the procedure can easily end up in a local minimum. To reduce the number of local minima, and thus the risk of misalignment, regularization terms based on prior knowledge can be incorporated in registration. We propose a regularization term that is based on statistical knowledge of the deformations that are to be expected. A statistical model, trained on the shapes of a set of segmentations, is integrated as a penalty term in a free-form registration framework. For the evaluation of our approach, we perform inter-patient registration of MR images, which were acquired for planning of radiation therapy of cervical cancer. The manual delineations of structures such as the bladder and the clinical target volume are available. For both structures, leave-one-patient-out registration experiments were performed. The propagated atlas segmentations were compared to the manual target segmentations by Dice similarity and Hausdorff distance. Compared with registration without the use of statistical knowledge, the segmentations were significantly improved, by 0.1 in Dice similarity and by 8 mm Hausdorff distance on average for both structures. © 2013 Elsevier Inc. All rights reserved

Multiatlas-based segmentation with preregistration atlas selection

Purpose: Automatic, atlas-based segmentation of medical images benefits from using multiple atlases, mainly in terms of robustness. However, a large disadvantage of using multiple atlases is the large computation time that is involved in registering atlas images to the target image. This paper aims to reduce the computation load of multiatlas-based segmentation by heuristically selecting atlases before registration. Methods: To be able to select atlases, pairwise registrations are performed for all atlas combinations. Based on the results of these registrations, atlases are clustered, such that each cluster contains atlas that registers well to each other. This can all be done in a preprocessing step. Then, the representatives of each cluster are registered to the target image. The quality of the result of this registration is estimated for each of the representatives and used to decide which clusters to fully register to the target image. Finally, the segmentations of the registered images are combined into a single segmentation in a label fusion procedure. Results: The authors perform multiatlas segmentation once with postregistration atlas selection and once with the proposed preregistration method, using a set of 182 segmented atlases of prostate cancer patients. The authors performed the full set of 182 leave-one-out experiments and in each experiment compared the result of the atlas-based segmentation procedure to the known segmentation of the atlas that was chosen as a target image. The results show that preregistration atlas selection is slightly less accurate than postregistration atlas selection, but this is not statistically significant. Conclusions: Based on the results the authors conclude that the proposed method is able to reduce the number of atlases that have to be registered to the target image with 80% on average, without compromising segmentation accuracy. © 2013 American Association of Physicists in Medicine

Evaluating and improving label fusion in atlas-based segmentation using the surface distance

Atlas-based segmentation is an increasingly popular method of automatically computing a segmentation. In the past, results of atlas-based segmentation have been evaluated using a volume overlap measure such as the Dice or Jaccard coefficients. However, in the first part of this paper we will argue and show that volume overlap measures are insensitive to local deviations. As a result, a segmentation that is judged to be of good quality when using such a measure may have large local deviations that may be problematic in clinical practice. In this paper, two versions of the surface distance are proposed as an alternative measure to evaluate the results of atlas-based segmentation, as they give more local information and therefore allow the detection of large local deviations. In most current atlas-based segmentation methods, the results of multiple atlases are combined to a single segmentation in a process called 'label fusion'. In a label fusion process it is important that segmentations with a high quality can be distinguished from those with a low quality. In the second part of the paper we will use the surface distance as a similarity measure during label fusion. We will present a modified version of the previously proposed SIMPLE algorithm, which selects propagated atlas segmentations based on their similarity with a preliminary estimate of the ground truth segmentation. The SIMPLE algorithm previously used the Dice coefficient as a similarity measure and in this paper we demonstrate that, using the spatial distance map instead, the results of atlas-based segmentation significantly improve. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE)