Organ shape reconstruction based on a single-projection image during treatment has wide clinical scope, e.g., in image-guided radiotherapy and surgical guidance. We propose an image-to-graph convolutional network that achieves deformable registration of a three-dimensional (3D) organ mesh for a low-contrast two-dimensional (2D) projection image. This framework enables simultaneous training of two types of transformation: from the 2D projection image to a displacement map, and from the sampled per-vertex feature to a 3D displacement that satisfies the geometrical constraint of the mesh structure. Assuming application to radiation therapy, the 2D/3D deformable registration performance is verified for multiple abdominal organs that have not been targeted to date, i.e., the liver, stomach, duodenum, and kidney, and for pancreatic cancer. The experimental results show shape prediction considering relationships among multiple organs can be used to predict respiratory motion and deformation from digitally reconstructed radiographs with clinically acceptable accuracy.

The positions of nodules can change because of intraoperative lung deflation, and the modeling of pneumothorax-associated deformation remains a challenging issue for intraoperative tumor localization. In this study, we introduce spatial and geometric analysis methods for inflated/deflated lungs and discuss heterogeneity in pneumothorax-associated lung deformation. Contrast-enhanced CT images simulating intraoperative conditions were acquired from live Beagle dogs. The images contain the overall shape of the lungs, including all lobes and internal bronchial structures, and were analyzed to provide a statistical deformation model that could be used as prior knowledge to predict pneumothorax. To address the difficulties of mapping pneumothorax CT images with topological changes and CT intensity shifts, we designed deformable mesh registration techniques for mixed data structures including the lobe surfaces and the bronchial centerlines. Three global-to-local registration steps were performed under the constraint that the deformation was spatially continuous and smooth, while matching visible bronchial tree structures as much as possible. The developed framework achieved stable registration with a Hausdorff distance of less than 1 mm and a target registration error of less than 5 mm, and visualized deformation fields that demonstrate per-lobe contractions and rotations with high variability between subjects. The deformation analysis results show that the strain of lung parenchyma was 35% higher than that of bronchi, and that deformation in the deflated lung is heterogeneous.

Respiratory motion and the associated deformations of abdominal organs and tumors are essential information in clinical applications. However, inter- and intra-patient multi-organ deformations are complex and have not been statistically formulated, whereas single organ deformations have been widely studied. In this paper, we introduce a multi-organ deformation library and its application to deformation reconstruction based on the shape features of multiple abdominal organs. Statistical multi-organ motion/deformation models of the stomach, liver, left and right kidneys, and duodenum were generated by shape matching their region labels defined on four-dimensional computed tomography images. A total of 250 volumes were measured from 25 pancreatic cancer patients. This paper also proposes a per-region-based deformation learning using the non-linear kernel model to predict the displacement of pancreatic cancer for adaptive radiotherapy. The experimental results show that the proposed concept estimates deformations better than general per-patient-based learning models and achieves a clinically acceptable estimation error with a mean distance of 1.2 ± 0.7 mm and a Hausdorff distance of 4.2 ± 2.3 mm throughout the respiratory motion.

Interactive image segmentation that uses a bounding box containing the foreground has gained great popularity because of its convenience. However, its performance is often degraded when the bounding box is not tight enough or covers large background regions. To solve this problem, this letter proposes a novel segmentation algorithm called “SuperCut”. This algorithm provides robust segmentation in one cut even with loose bounding boxes. In the proposed SuperCut, superpixels of the whole image are first computed, and then a background similarity map for the superpixels inside the bounding box is created based on Haar-Wavelet feature and pixel intensities. Finally, a max-flow cut is made based on the map. The background similarity map reduces the influence from possible background regions inside the bounding box, which leads to the efficient segmentation of SuperCut. To evaluate the effectiveness of the proposed SuperCut, we compared it with some recent published interactive image segmentation techniques. The experimental results show that the proposed SuperCut outperforms other compared methods especially when using loose bounding boxes.

In this paper, we propose a method for reducing the computational cost of strong coupling for multiscale cardiovascular simulation models. In such a model, individual model modules of myocardial cell, left ventricular structural dynamics, and circulatory hemodynamics are coupled. The strong coupling method enables stable and accurate calculation, but requires iterative calculations which are computationally expensive. The iterative calculations can be reduced, if accurate initial approximations are made available by predictors. The proposed method uses the Kalman filter to estimate accurate predictions by filtering out noise included in past values. The performance of the proposed method was assessed with an application to a previously published multiscale cardiovascular model. The proposed method reduced the number of iterations by 90% and 62% compared with no prediction and Lagrange extrapolation, respectively. Even when the parameters were varied and number of elements of the left ventricular finite-element model increased, the number of iterations required by the proposed method was significantly lower than that without prediction. These results indicate the robustness, scalability, and validity of the proposed method.

This paper introduces a concept of endoscopic image augmentation that overlays shape changes to support cutting procedures. This framework handles the history of measured drill tip's location as a volume label, and visualizes the remains to be cut overlaid on the endoscopic image in real time. We performed a cutting experiment, and the efficacy of the cutting aid was evaluated among shape similarity, total moved distance of a cutting tool, and the required cutting time. The results of the experiments showed that cutting performance was significantly improved by the proposed framework.

This study proposes a method to estimate elastic deformation using silhouettes obtained from multiple endoscopic images. Our method can estimate the intraoperative deformation of organs using a volumetric mesh model reconstructed from preoperative CT data. We use this elastic body silhouette information of elastic bodies not to model the shape but to estimate the local displacements. The model shape is updated to satisfy the silhouette constraint while preserving the shape as much as possible. The result of the experiments showed that the proposed methods could estimate the deformation with root mean square (RMS) errors of 5.0–10 mm.

In preoperative planning for mandibular reconstructive surgery, it is necessary to determine the osteotomy lines for fibular shaping and the proper placement of fibular segments in the mandible. Although virtual surgical planning has been utilized in preoperative decision making, current software designs require manual operation and a trial-and-error process to refine the reconstruction plan. We have developed volumetric fibular transfer simulation software that can quickly design a preoperative plan based on direct volume manipulation and quantitative comparison with the patient's original mandible. We propose three quantitative shape indicators-volume ratio, contour error, and maximum projection-for symmetrical lesions of the mandible, and have implemented a parallel computation algorithm for the semiautomatic placement of fibular segments. Using this virtual planning software, we conducted a retrospective study of the computed tomography data from nine patients. We found that combining direct volume manipulation with real-time local search of placement improved the applicability of the planning system to optimize mandibular reconstruction.

BACKGROUND: Recent advances in microscopy enable the acquisition of large numbers of tomographic images from living tissues. Three-dimensional microscope images are often displayed with volume rendering by adjusting the transfer functions. However, because the emissions from fluorescent materials and the optical properties based on point spread functions affect the imaging results, the intensity value can differ locally, even in the same structure. Further, images obtained from brain tissues contain a variety of neural structures such as dendrites and axons with complex crossings and overlapping linear structures. In these cases, the transfer functions previously used fail to optimize image generation, making it difficult to explore the connectivity of these tissues. RESULTS: This paper proposes an interactive visual exploration method by which the transfer functions are modified locally and interactively based on multidimensional features in the images. A direct editing interface is also provided to specify both the target region and structures with characteristic features, where all manual operations can be performed on the rendered image. This method is demonstrated using two-photon microscope images acquired from living mice, and is shown to be an effective method for interactive visual exploration of overlapping similar structures. CONCLUSIONS: An interactive visualization method was introduced for local improvement of visualization by volume rendering in two-photon microscope images containing regions in which linear nerve structures crisscross in a complex manner. The proposed method is characterized by the localized multidimensional transfer function and interface where the parameters can be determined by the user to suit their particular visualization requirements.