Automated Registration of Whole-Body Follow-Up MicroCT Data of Mice

Baiker, Martin; Staring, Marius; Löwik, Clemens W.G.M.; Reiber, Johan H. C.; Lelieveldt, Boudewijn P. F.

doi:10.1007/978-3-642-23629-7_63

Cited by 27 publications

(22 citation statements)

References 13 publications

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“…Guidance error is expressed as the mean Euclidean distance between the locations of the center of the external markers in the transformed source and target image that needs to be minimized (Baiker et al 2011). The definitions of all objectives can be found in the elastix manual.…”

Section: Registration Experiments and Setupmentioning

confidence: 99%

The feasibility of manual parameter tuning for deformable breast MR image registration from a multi-objective optimization perspective

et al. 2017

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Deformable image registration is typically formulated as an optimization problem involving a linearly weighted combination of terms that correspond to objectives of interest (e.g. similarity, deformation magnitude). The weights, along with multiple other parameters, need to be manually tuned for each application, a task currently addressed mainly via trial-and-error approaches. Such approaches can only be successful if there is a sensible interplay between parameters, objectives, and desired registration outcome. This, however, is not well established. To study this interplay, we use multi-objective optimization, where multiple solutions exist that represent the optimal trade-offs between the objectives, forming a so-called Pareto front. Here, we focus on weight tuning. To study the space a user has to navigate during manual weight tuning, we randomly sample multiple linear combinations. To understand how these combinations relate to desirability of registration outcome, we associate with each outcome a mean target registration error (TRE) based on expert-defined anatomical landmarks. Further, we employ a multi-objective evolutionary algorithm that optimizes the weight combinations, yielding a Pareto front of solutions, which can be directly navigated by the user. To study how the complexity of manual weight tuning changes depending on the registration problem, we consider an easy problem, prone-to-prone breast MR image registration, and a hard problem, prone-to-supine breast MR image registration. Lastly, we investigate how guidance information as an additional objective influences the prone-to-supine registration outcome. Results show that the interplay between weights, objectives, and registration outcome makes manual weight tuning feasible for the prone-to-prone problem, but very challenging for the harder prone-to-supine problem. Here, patient-specific, multi-objective weight optimization is needed, obtaining a mean TRE of 13.6 mm without guidance information reduced to 7.3 mm with guidance information, but also providing a Pareto front that exhibits an intuitively sensible interplay between weights, objectives, and registration outcome, allowing outcome selection.

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Section: Registration Experiments and Setupmentioning

confidence: 99%

The feasibility of manual parameter tuning for deformable breast MR image registration from a multi-objective optimization perspective

et al. 2017

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“…Suh et al 16 proposed a method based on a combined surface registration using both bone and body surfaces for small animal whole body registration. Baiker et al 17 registered the skeleton, the lungs and the skin of an articulated animal atlas, yielding point correspondence of those structures in the first step. This correspondence is then used to regularize an intensity-based Bspline registration in the next step.…”

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confidence: 99%

CT-PET weighted image fusion for separately scanned whole body rat

et al. 2012

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Purpose:The limited resolution and lack of spatial information in positron emission tomography (PET) images require the complementary anatomic information from the computed tomography (CT) and/or magnetic resonance imaging (MRI). Therefore, multimodality image fusion techniques such as PET/CT are critical in mapping the functional images to structural images and thus facilitate the interpretation of PET studies. In our experimental situation, the CT and PET images are acquired in separate scanners at different times and the inherent differences in the imaging protocols produce significant nonrigid changes between the two acquisitions in addition to dissimilar image characteristics. The registration conditions are also poor because CT images have artifacts due to the limitation of current scanning settings, while PET images are very blurry (in transmission-PET) and have vague anatomical structure boundaries (in emission-PET). Methods: The authors present a new method for whole body small animal multimodal registration. In particular, the authors register whole body rat CT image and PET images using a weighted demons algorithm. The authors use both the transmission-PET and the emission-PET images in the registration process emphasizing particular regions of the moving transmission-PET image using the emission-PET image. After a rigid transformation and a histogram matching between the CT and the transmission-PET images, the authors deformably register the transmission-PET image to the CT image with weights based on the intensity-normalized emission-PET image. For the deformable registration process, the authors develop a weighted demons registration method that can give preferences to particular regions of the input image using a weight image. Results: The authors validate the results with nine rat image sets using the M-Hausdorff distance (M-HD) similarity measure with different outlier-suppression parameters (OSP). In comparison with standard methods such as the regular demons and the normalized mutual information (NMI)-based nonrigid free-form deformation (FFD) registration, the proposed weighted demons registration method shows average M-HD errors: 3.99 6 1.37 (OSP ¼ 10), 5.04 6 1.59 (OSP ¼ 20) and 5.92 6 1.61 (OSP ¼ 1) with statistical significance (p < 0.0003) respectively, while NMI-based nonrigid FFD has average M-HD errors: 5.74 6 1.73 (OSP ¼ 10), 7.40 6 7.84 (OSP ¼ 20) and 9.83 6 4.13 (OSP ¼ 1), and the regular demons has average M-HD errors: 6.79 6 0.83 (OSP ¼ 10), 9.19 6 2.39 (OSP ¼ 20) and 11.63 6 3.99 (OSP ¼ 1), respectively. In addition to M-HD comparisons, the visual comparisons on the faint-edged region between the CT and the aligned PET images also show the encouraging improvements over the other methods. Conclusions: In the whole body multimodal registration between CT and PET images, the utilization of both the transmission-PET and the emission-PET images in the registration process by emphasizing particular regions of the transmission-PET image using an emission-PET image is effective. This me...

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“…An integration of our passive contour distance into the intensity-based registration of [3] is particularly promising.…”

Section: Discussionmentioning

confidence: 99%

“…A model based registration with a subsequent intensity-based regis tration for whole-body segmentation in Micro-CT data of mice was introduced by Baiker et al [3]. They achieve high accuracies for skin and skeleton, but do not report results for inner organs which are the focused area of this work.…”

Section: Introductionmentioning

confidence: 97%