Coherent Point Drift Registration Research Articles

Landmark-free, parametric hypothesis tests regarding two-dimensional contour shapes using coherent point drift registration and statistical parametric mapping.

This paper proposes a computational framework for automated, landmark-free hypothesis testing of 2D contour shapes (i.e., shape outlines), and implements one realization of that framework. The proposed framework consists of point set registration, point correspondence determination, and parametric full-shape hypothesis testing. The results are calculated quickly (<2 s), yield morphologically rich detail in an easy-to-understand visualization, and are complimented by parametrically (or nonparametrically) calculated probability values. These probability values represent the likelihood that, in the absence of a true shape effect, smooth, random Gaussian shape changes would yield an effect as large as the observed one. This proposed framework nevertheless possesses a number of limitations, including sensitivity to algorithm parameters. As a number of algorithms and algorithm parameters could be substituted at each stage in the proposed data processing chain, sensitivity analysis would be necessary for robust statistical conclusions. In this paper, the proposed technique is applied to nine public datasets using a two-sample design, and an ANCOVA design is then applied to a synthetic dataset to demonstrate how the proposed method generalizes to the family of classical hypothesis tests. Extension to the analysis of 3D shapes is discussed.

Segmentation of hippocampus guided by assembled and weighted coherent point drift registration

Segmentation of the subcortical structures in the brain such as the hippocampus, is known to be very challenging owing to its’ image characteristics. In brain MR images, the hippocampus is observed as a gray matter structure that often exhibits very weak or unclear boundary definitions at some fragments of its’ boundary. The unclear boundaries even cause the medical experts to misjudge the hippocampus boundary, especially at the head and tail. In this research, an automated segmentation approach, termed as Assembled and Weighted Coherent Point Drift is investigated to delineate the hippocampus accurately. Evaluations on public datasets produced an average Dice Similarity Coefficient of 0.8050, which appears better, in comparison to several other hippocampus segmentation approaches, especially against the well-known software program called Freesurfer. The study also revealed that the accuracy of the proposed segmentation approach seems on par with other various state-of-the-art approaches.

Refinement of the Coherent Point Drift Registration Results by the Example of Cephalometry Problems

Personalizing the three-dimensional atlas model (template) of an organ under analysis based on the data of patient’s three-dimensional examination is an important task for modern digital medicine. The template should be represented by a set of points Y and marked with the key points for the model (landmarks). The template is personalized by the non-rigid registration of the set Y with the set of points X that represents patient’s tomogram. Presently, the coherent point drift (CPD) is the most popular method for solving the problem of non-rigid alignment. In this paper, we propose and explore an approach that substantially improves the CPD result in the problem of automatic registration of cephalometric points (CPs). The proposed algorithm remains robust in the presence of significant skull deformations. Traditionally, 3D cephalometry uses the geometric descriptor of a CP, which refines the position of the point on the bone surface. However, the result of applying the descriptor depends significantly on the accuracy of its anatomical basis reconstruction. The proposed approach solves this problem by clarifying the basis of geometric descriptors, the supporting elements of which are orbital planes and the Frankfurt (orbital-ear) horizontal. For this purpose, additionally marked points of the orbitals (YO) are included into the template Y. Once Y and X are aligned by the CPD method, the plane positions of the orbitals are refined by solving a linear regression problem on the subsets of YO for the left and right orbitals. Cases of refinement with and without use of Tikhonov regularization (ridge-regression) are analyzed. The effect of the increase in the cardinality of the set X relative to Y on the registration accuracy is investigated. It is found that the condition |X| < |Y| has a negative effect on the accuracy, which increases when the cardinality of X relative to Y decreases. The refinement of CPs by the geometric descriptor is carried out on tomogram data in the region around CPs that is found by the CPD method. The dimensions of this region along three coordinates are determined by the anatomical domain of a particular CP descriptor. The quality of the algorithm is measured by the Euclidean distance between hand-marked and automatically found points. The template Y for the algorithm is built on a trauma-deformed skull. The algorithm is quantitatively verified by registering the CPs of orbitals and cheekbones from the data of four tomograms for the deformed skull. A key feature and source of high accuracy of the approach is the use of linear regression with Tikhonov regularization to refine it. As a result, 81.25% of the points found fall within a radius of 2 mm from the hand-marked points and 100% of the points fall within a radius of 4 mm.

Introduction of a hybrid treatment delivery system used for quality assurance in multi-catheter interstitial brachytherapy

Multi-catheter interstitial brachytherapy (iBT) is a treatment option for breast cancer patients after breast conserving surgery. Typically, only a few additional quality interventions after the first irradiation have been introduced to ensure the planned treatment delivery. Therefore, the purpose of this study is to show the possibilities of an electromagnetic tracking (EMT) system integrated into the afterloader for quality assurance (QA) in high-dose rate (HDR) iBT of patients with breast cancer.The hybrid afterloader system equipped with an electromagnetic sensor was used for all phantom and patient measurements. Phantom measurements were conducted to estimate the quality of different evaluation schemes. After a coherent point drift registration of the EMT traces to the reconstructed catheters based on computed tomograms the dwell positions (DP) were defined. Different fitting and interpolation methods were analyzed for the reconstruction of DPs. All estimated DPs were compared to the DPs defined in treatment planning.Until now, the implant geometry of 20 patients treated with HDR brachytherapy was acquired and explored.Regarding the reconstruction techniques, both fitting and interpolation were able to detect manually introduced shifts and swaps. Nonetheless, interpolation showed superior results (RMSE = 1.27 mm), whereas fitting seemed to be more stable to distortion and motion.The EMT system proved to be beneficial for QA in brachytherapy and furthermore, clinical feasibility was proven.

Refinement of the Coherent Point Drift Registration Results by the Example of Cephalometry Problems

Refinement of the Coherent Point Drift Registration Results by the Example of Cephalometry Problems

Evaluation of a Coherent Point Drift Algorithm for Breast Image Registration via Surface Markers

Breast Magnetic Resonance Imaging (MRI) is a reliable imagingtool for localization and evaluation of lesions prior to breast conservingsurgery (BCS). MR images typically will be used to determinethe size and location of the tumours before making the incisionin order to minimize the amount of tissue excised.The arm position and configuration of the breast during andprior to surgery are different and one question is whether it wouldbe possible to match the two configurations. This matching processcan potentially be used in development of tools to guide surgeonsin the incision process.Recently, a Thin-Plate-Spline (TPS) algorithm has been proposedto assess the feasibility of breast tissue matching using fiducialsurface markers in two different arm positions. The registrationalgorithm uses the surface markers only and does not employ theimage intensities.In this manuscript, we apply and evaluate a coherent point drift(CPD) algorithm for registration of three-dimensional breast MR imagesof six patient volunteers. In particular, we evaluate the resultsof the previous TPS registration technique to the proposed rigidCPD, affine CPD, and deformable CPD registration algorithms onthe same patient datasets.The preliminary results suggest that the CPD deformable registrationalgorithm is superior in correcting the motion of the breastcompared to CPD rigid, affine and TPS registration algorithms.

SaCoseg: object cosegmentation by shape conformability.

In this paper, an object cosegmentation method based on shape conformability is proposed. Different from the previous object cosegmentation methods which are based on the region feature similarity of the common objects in image set, our proposed SaCoseg cosegmentation algorithm focuses on the shape consistency of the foreground objects in image set. In the proposed method, given an image set where the implied foreground objects may be varied in appearance but share similar shape structures, the implied common shape pattern in the image set can be automatically mined and regarded as the shape prior of those unsatisfactorily segmented images. The SaCoseg algorithm mainly consists of four steps: 1) the initial Grabcut segmentation; 2) the shape mapping by coherent point drift registration; 3) the common shape pattern discovery by affinity propagation clustering; and 4) the refinement by Grabcut with common shape constraint. To testify our proposed algorithm and establish a benchmark for future work, we built the CoShape data set to evaluate the shape-based cosegmentation. The experiments on CoShape data set and the comparison with some related cosegmentation algorithms demonstrate the good performance of the proposed SaCoseg algorithm.

Image Registration Technique Based on a Fast CPD Algorithm

The Coherent Point Drift (CPD) algorithm which based on Gauss Mixture Model is a robust point set registration algorithm. However, the selection of robustness weight which used to describe the noise may directly affect the point set registration efficiency. For resolving the problem, this paper presents a CPD registration algorithm which based on distance threshold constraint. Before the point set registration, the inaccurate template point set by resampling become the initial point set of point set matching, in order to eliminate some points that the distance to target point set is too close and too far in the inaccurate template point set, and set the weights of robustness as . In the simulation experiments, we make two group experiments: the first group is the registration of the inaccurate template point set and the accurate target point set, while the second group is the registration of the accurate template point set and the accurate target point set. The results of comparison show that our method can solve the problem of selection for the weight. And it improves the speed and precision of the original CPD registration.