Adaptive radiation therapy uses deformable image registration to warp the dose from the planning computed tomography (CT) image to the daily cone-beam CT (CBCT) image acquired using the onboard volumetric imaging. Image quality of this CBCT image is usually inferior due to poor soft-tissue contrast of organs such as the prostate, causing registration algorithms to underperform in terms of accuracy. To alleviate this problem, we develop a hybrid image-similarity cost function that incorporates a point-to-distance map (PD) metric as one of its components. Given a pair of segmented images, structures on the fixed image are represented as sets of points while structures on the moving image are described as distance maps. The total distance of all fixed points to their associated boundaries on the moving image constitutes the PD metric, which is combined with the more traditional intensity similarity metric between the fixed and moving images. In this work, we use cubic B-splines as the registration transform. Our approach is validated using the pelvic reference dataset wherein the prostate, bladder, and rectum are manually contoured from the CT and CBCT images by a medical expert to obtain the segmented fixed and moving images. Accuracy of the deformable registration is quantified using the Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (95% HD), with and without the PD metric. Results demonstrate much improved overlap between the fixed and warped contours once the PD metric is applied. Moreover, the computational overhead associated with adding the PD metric is minimal.
In this paper, we present a model to obtain prior knowledge for organ localization in CT thorax images using three dimensional convolutional neural networks (3D CNNs). Specifically, we use the knowledge obtained from CNNs in a Bayesian detector to establish the presence and location of a given target organ defined within a spherical coordinate system. We train a CNN to perform a soft detection of the target organ potentially present at any point, x = [r,Θ,Φ]T. This probability outcome is used as a prior in a Bayesian model whose posterior probability serves to provide a more accurate solution to the target organ detection problem. The likelihoods for the Bayesian model are obtained by performing a spatial analysis of the organs in annotated training volumes. Thoracic CT images from the NSCLC–Radiomics dataset are used in our case study, which demonstrates the enhancement in robustness and accuracy of organ identification. The average value of the detector accuracies for the right lung, left lung, and heart were found to be 94.87%, 95.37%, and 90.76% after the CNN stage, respectively. Introduction of spatial relationship using a Bayes classifier improved the detector accuracies to 95.14%, 96.20%, and 95.15%, respectively, showing a marked improvement in heart detection. This workflow improves the detection rate since the decision is made employing both lower level features (edges, contour etc) and complex higher level features (spatial relationship between organs). This strategy also presents a new application to CNNs and a novel methodology to introduce higher level context features like spatial relationship between objects present at a different location in images to real world object detection problems.
In this paper we propose a new strategy for the recovery of complex anatomical deformations that exhibit local discontinuities, such as the shearing found at the lung-ribcage interface, using multi-grid octree B-splines. B- spline based image registration is widely used in the recovery of respiration induced deformations between CT images. However, the continuity imposed upon the computed deformation field by the parametrizing cubic B- spline basis function results in an inability to correctly capture discontinuities such as the sliding motion at organ boundaries. The proposed technique efficiently captures deformation within and at organ boundaries without the need for prior knowledge, such as segmentation, by selectively increasing deformation freedom within image regions exhibiting poor local registration. Experimental results show that the proposed method achieves more physically plausible deformations than traditional global B-spline methods.
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