Medical Image Registration Guided by Application-Specific Geometry

Image registration is an important task in medical image processing. Among its applications are inter-patient registration to perform segmentation of organs, registration of follow-up scans to propagate the in tissue accumulated radiation dose of a radiotherapy, and registration to perform deformation analysis over time or within a population. Generally, depending on the type of images and the nature of the spatial variation between the images, application specific registration settings need to be chosen, such as the image similarity metric, the type of optimizer and the number of degrees of freedom for the transformation model. For various sorts of applications the options available in a general registration algorithm are limited to obtain good registration results since they do not exploit application specific geometry knowledge. Specific applications can benefit from prior shape knowledge of a population, geometric properties of structures in the image, or knowledge about discontinuities in the deformation field. In this thesis various extensions to a general registration algorithm are proposed to tailor the algorithm to the issues in the applications involved. The method proposed in Chapter 2 deals with inter-patient registration of patients with cervical cancer. Between patients are large variability in organ shape and position is observed that requires large and complex deformations. To guide registration in finding these deformations a statistical shape, trained on the shape of the segmentations of the population, is incorporated as penalty term in the optimization process. In Chapter 3 intra-patient registration of the images acquired for external beam radiation therapy and brachytherapy is performed. The missing volume of the applicator, as used in cervical brachytherapy, is modeled as a surface mesh and its volume is minimized during registration. For registration of images with sliding organs, in Chapter 4 we propose a new transformation model that is accompanied by a geometric penalty term. The proposed method is applied on inhale-exhale CT scans in which the lungs slide along the thoracic cage. Additionally, examples of registration of synthetic images and a registration of a patient with cervical cancer are given. Chapter 5 proposes a improved similarity metric for groupwise registration. The computation of the groupwise metric that consists of the normalized cross-correlation between all image pairs in the group is reduced to a linear complexity. In the last Chapter, Chapter 6, a summary and a general discussion is given

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

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

Improving label fusion in multi-atlas based segmentation by locally combining atlas selection and performance estimation

In multi-atlas based segmentation, a target image is segmented by registering multiple atlas images to this target image and propagating the corresponding atlas segmentations. These propagated segmentations are then combined into a single segmentation in a process called label fusion. Multi-atlas based segmentation is a segmentation method that allows fully automatic segmentation of image populations that exhibit a large variability in shape and image quality. Fusing the results of multiple atlases makes this technique robust and reliable. Previously, we have presented the SIMPLE method for label fusion and have shown that it outperforms existing methods. However, the downside of this method is its computation time and the fact that it requires a large atlas set. This is not always a problem, but in some cases segmentation may be time-critical or large atlas sets are not available. This paper presents a new label fusion method which is a local version of the SIMPLE method that has two advantages: when a large atlas set is available it improves the accuracy of label fusion and when this is not the case it gives the same accuracy as the original SIMPLE method, but with considerably fewer atlases. This is made possible by better utilizing the local information contained in propagated segmentations that would otherwise be discarded. Our method (semi-)automatically divides the propagated segmentations in multiple regions. A label fusion process can then be applied to each of these regions separately and the end result can be reconstructed out of multiple partial results. We demonstrate that the number of atlases needed can be reduced to 20 atlases without compromising segmentation quality. Our method is validated in an application to segmentation of the prostate, using an atlas set of 125 manually segmented images

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

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

Label fusion in multi-atlas based segmentation with user-defined local weights

Multi-atlas based segmentation is a popular method to automatically segment a target image, in which the correspondence to already segmented atlas images is used to construct multiple segmentations for a single structure in the target image. These multiple segmentations are then combined into a single segmentation for the target image in a process called label fusion. In the past, the result of multi-atlas based segmentation has mostly been evaluated using a volume overlap measure. However, such a measure can only be used to assess the global quality of a segmentation and does not take into account local differences in for example the clinical relevance of a certain region of the segmentation. We propose to use voxel-based weights in the evaluation of segmentations and show that by using these weights already during the label fusion process, one is able to obtain multi-atlas based segmentation results with an improved clinical relevance compared to unweighted atlas based segmentation. A method is proposed to implement this for multi-atlas based segmentation of the prostate. © 2011 IEEE

Fully automated segmentation of the left atrium, pulmonary veins, and left atrial appendage from magnetic resonance angiography by joint‐atlas‐optimization

Cardiovascular Aspects of Radiolog