waashk/dblp huggingface dataset:
utility dependence in correct and fair rational secret sharing. the problem of carrying out cryptographic computations when the participating parties are rational in a game-theoretic sense has recently gained much attention. one problem that has been studied considerably is that of rational secret sharing. in this setting, the aim is to construct a mechanism (protocol) so that parties behaving rationally have incentive to cooperate and provide their shares in the reconstruction phase, even if each party prefers to be the only one to learn the secret.although this question was only recently asked by halpern and teague (stoc 2004), a number of works with beautiful ideas have been presented to solve this problem. however, they all have the property that the protocols constructed need to know the actual utility values of the parties (or at least a bound on them). this assumption is very problematic because the utilities of parties are not public knowledge. we ask whether this dependence on the actual utility values is really necessary and prove that in the basic setting, rational secret sharing cannot be achieved without it. on the positive side, we show that by somewhat relaxing the standard assumptions on the utility functions, it is possible to achieve utility independence. in addition to the above, observe that the known protocols for rational secret sharing that do not assume simultaneous channels all suffer from the problem that one of the parties can cause the others to output an incorrect value. (this problem arises when a party gains higher utility by having another output an incorrect value than by learning the secret itself; we argue that such a scenario is not at all unlikely.) we show that this problem is inherent in the non-simultaneous channels model, unless the actual values of the parties' utilities from this attack is known, in which case it is possible to prevent this from happening.
somewhat non-committing encryption and efficient adaptively secure oblivious transfer. designing efficient cryptographic protocols tolerating adaptive adversaries, who are able to corrupt parties on the fly as the computation proceeds, has been an elusive task. in this paper we make progress in this area. first, we introduce a new notion called semi-adaptive security which is slightly stronger than static security but significantly weaker than fully adaptive security. the main difference between adaptive and semi-adaptive security is that semi-adaptive security allows for the case where one party starts out corrupted and the other party becomes corrupted later on, but not the case where both parties start out honest and become corrupted later on. as such, semi-adaptive security is much easier to achieve than fully adaptive security. we then give a simple, generic protocol compiler which transforms any semi-adaptively secure protocol into a fully adaptively secure one. the compilation effectively decomposes the problem of adaptive security into two (simpler) problems which can be tackled separately: the problem of semi-adaptive security and the problem of realizing a weaker variant of secure channels.we solve the latter problem by means of a new primitive that we call somewhat non-committing encryption resulting in significant efficiency improvements over the standard method for realizing secure channels using (fully) non-committing encryption. somewhat non-committing encryption has two parameters: an equivocality parameter ¿ (measuring the number of ways that a ciphertext can be "opened") and the message sizes k. our implementation is very efficient for small values ¿, even when k is large. this translates into a very efficient compilation of semi-adaptively secure protocols for tasks with small input/output domains (such as bit-ot) into fully adaptively secure protocols.indeed, we showcase our methodology by applying it to the recent oblivious transfer protocol by peikert etal [crypto 2008], which is only secure against static corruptions, to obtain the first efficient, adaptively secure and composable ot protocol. in particular, to transfer an n-bit message, we use a constant number of rounds and o(n) public key operations.
nonrigid registration of myocardial perfusion mri using pseudo ground truth. in this paper we present a method for nonrigid registration of myocardial perfusion mr images. instead of registering pairs of images within the observed sequence, we register the observed sequence to a pseudo ground truth, which is a motion/noise-free sequence estimated from the observed one. as the corresponding images of the two sequences have almost identical intensity distributions, our method overcomes the challenges arising from rapidly varying image intensity and contrast. the pseudo ground truth and the deformation fields for the observed sequence are obtained simultaneously by minimizing an energy functional integrating both the registration error and the spatiotemporal constraints on the pseudo ground truth in an expectation-maximization fashion. we have tested the proposed nonrigid registration method on real cardiac mr perfusion scans, both qualitatively and quantitatively. experimental results show that the proposed method is able to successfully compensate for the heart motion during contrast enhancement.
multimodal image registration by information fusion at feature level. this paper proposes a novel multimodal image registration method which can fully utilize the multimodal information and result in a more accurate unified deformation field. different from the existing methods which fuse the information at the image/intensity level, the proposed method fuses the multimodal information at the feature level through gabor wavelets transformation. at this level, complementary and redundant information is distinguished reliably and efficiently, and then combined and removed respectively. experiments on both simulated and real t1+dti image sets illustrate that the proposed method can effectively incorporate better characterization for white matter (wm) from the dti and for gray matter (gm) from the t1 image and lead to a more accurate and efficient multimodal image registration which paves the way for the subsequent multimodal population-based studies.
graphical models and deformable diffeomorphic population registration using global and local metrics. in this paper we propose a novel framework to unite a population to an optimal (unknown) pose through their mutual deformation. the registration criterion comprises three terms, the first imposes compactness on appearance of the registered population at the pixel level, the second tries to minimize the individual distances between all possible pairs of images, while the last is a regularization one imposing smoothness on the deformation fields. the problem is reformulated as a graphical model that consists of hidden (deformation fields) and observed variables (intensities). a novel deformation grid-based scheme is proposed that guarantees the diffeomorphism of the deformation and is computationally favorably compared to standard deformation methods. towards addressing important deformations we propose a compositional approach where the deformations are recovered through the sub-optimal solutions of successive discrete mrfs by using efficient linear programming. promising experimental results using real 2d data demonstrate the potentials of our approach.
efficient large deformation registration via geodesics on a learned manifold of images. geodesic registration methods have been used to solve the large deformation registration problems, which are hard to solve with conventional registration methods. however, analytically defined geodesics may not coincide with anatomically optimal paths of registration. in this paper we propose a novel and efficient method for large deformation registration by learning the underlying structure of the data using a manifold learning technique. in this method a large deformation between two images is decomposed into a series of small deformations along the shortest path on the graph that approximates the metric structure of data. furthermore, the graph representation allows us to estimate the optimal group template by minimizing geodesic distances. we demonstrate the advantages of the proposed method with synthetic 2d images and real 3d mice brain volumes.
characterization of anatomic fiber bundles for diffusion tensor image analysis. in this paper we deal with the problem of quantification of diffusion tensor (dt) data sets. a set of measures and a 2d tract mapping technique are proposed to analyze the fiber structures in brain white matter and to allow for comparisons between different subjects, either patients or controls. features such as integrity, discontinuity and connectivity of the fiber bundles are proposed and analyzed, taking into account longitudinal and transverse information of the fiber bundle under study. the performance of the proposed characterization framework is shown analyzing the corticospinal tracts of control data sets and pathological cases, comparing the measures between controls and patients and also between the right and left hemispheres. a reproducibility study is also performed to show the robustness of the proposed measures.
hierarchical normalized cuts: unsupervised segmentation of vascular biomarkers from ovarian cancer tissue microarrays. research has shown that tumor vascular markers (tvms) may serve as potential oca biomarkers for prognosis prediction. one such tvm is esm-1, which can be visualized by staining ovarian tissue microarrays (tma) with an antibody to esm-1. the ability to quickly and quantitatively estimate vascular stained regions may yield an image based metric linked to disease survival and outcome. automated segmentation of the vascular stained regions on the tmas, however, is hindered by the presence of spuriously stained false positive regions. in this paper, we present a general, robust and efficient unsupervised segmentation algorithm, termed hierarchical normalized cuts (hncut), and show its application in precisely quantifying the presence and extent of a tvm on oca tmas. the strength of hncut is in the use of a hierarchically represented data structure that bridges the mean shift (ms) and the normalized cuts (ncut) algorithms. this allows hncut to efficiently traverse a pyramid of the input image at various color resolutions, efficiently and accurately segmenting the object class of interest (in this case esm-1 vascular stained regions) by simply annotating half a dozen pixels belonging to the target class. quantitative and qualitative analysis of our results, using 100 pathologist annotated samples across multiple studies, prove the superiority of our method (sensitivity 81%, positive predictive value (ppv), 80%) versus a popular supervised learning technique, probabilistic boosting trees (sensitivity, ppv of 76% and 66%).
estimating continuous 4d wall motion of cerebral aneurysms from 3d rotational angiography. this paper presents a technique to recover dynamic 3d vascular morphology from a single 3d rotational x-ray angiography acquisition. the dynamic morphology corresponding to a canonical cardiac cycle is represented via a 4d b-spline based spatiotemporal deformation. such deformation is estimated by simultaneously matching the forward projections of a sequence of the temporally deformed 3d reference volume to the entire 2d measured projection sequence. a joint use of two acceleration strategies is also proposed: semi-precomputation of forward projections and registration metric computation based on a narrow-band region-of-interest. digital and physical phantoms of pulsating cerebral aneurysms have been used for evaluation. accurate estimation has been obtained in recovering sub-voxel pulsation, even from images with substantial intensity inhomogeneity. results also demonstrate that the acceleration strategies can reduce memory consumption and computational time without degrading the performance.
toward real-time simulation of blood-coil interaction during aneurysm embolization. over the last decade, remarkable progress has been made in the field of endovascular treatment of aneurysms. technological advances continue to enable a growing number of patients with cerebral aneurysms to be treated with a variety of endovascular strategies, essentially using detachable platinum coils. yet, coil embolization remains a very complex medical procedure for which careful planning must be combined with advanced technical skills in order to be successful.in this paper we propose a method for computing the complex blood flow patterns that take place within the aneurysm, and for simulating the interaction of coils with this flow. this interaction is twofold, first involving the impact of the flow on the coil during the initial stages of its deployment, and second concerning the decrease of blood velocity within the aneurysm, as a consequence of coil packing. we also propose an approach to achieve real-time computation of coil-flow bilateral influence, necessary for interactive simulation. this in turns allows to dynamically plan coil embolization for two key steps of the procedure: choice and placement of the first coils, and assessment of the number of coils necessary to reduce aneurysmal blood velocity and wall pressure.
a meta registration framework for lesion matching. a variety of pixel and feature based methods have been proposed for registering multiple views of anatomy visible in studies obtained using diagnostic, minimally invasive imaging. a given registration method may outperform another depending on anatomical variations, imaging conditions, and imaging sensor performance, and it is often difficult a priori to determine the best registration method for a particular application. to address this problem, we propose a registration framework that pools the results of multiple registration methods using a decision function for validating registrations. we refer to this as meta registration. we demonstrate that our framework outperforms several individual registration methods on the task of registering multiple views of crohn's disease lesions sampled from a capsule endoscopy (ce) study database. we also report on preliminary work on assessing the quality of registrations obtained, and the possibility of using such assessment in the registration framework.
fast automatic segmentation of the esophagus from 3d ct data using a probabilistic model. automated segmentation of the esophagus in ct images is of high value to radiologists for oncological examinations of the mediastinum. it can serve as a guideline and prevent confusion with pathological tissue. however, segmentation is a challenging problem due to low contrast and versatile appearance of the esophagus. in this paper, a two step method is proposed which first finds the approximate shape using a "detect and connect" approach. a classifier is trained to find short segments of the esophagus which are approximated by an elliptical model. recently developed techniques in discriminative learning and pruning of the search space enable a rapid detection of possible esophagus segments. prior shape knowledge of the complete esophagus is modeled using a markov chain framework, which allows efficient inferrence of the approximate shape from the detected candidate segments. in a refinement step, the surface of the detected shape is non-rigidly deformed to better fit the organ boundaries. in contrast to previously proposed methods, no user interaction is required. it was evaluated on 117 datasets and achieves a mean segmentation error of 2.28mm with less than 9s computation time.
3d meshless prostate segmentation and registration in image guided radiotherapy. image guided radiation therapy (igrt) improves radiation therapy for prostate cancer by facilitating precise radiation dose coverage of the object of interest, and minimizing dose to adjacent normal organs. in an effort to optimize igrt, we developed a fast segmentation-registration-segmentation framework to accurately and efficiently delineate the clinically critical objects in cone beam ct images obtained during radiation treatment. the proposed framework started with deformable models automatically segmenting the prostate, bladder, and rectum in planning ct images. all models were built around seed points and involved in the ct image under the influence of image features using the level set formulation. the deformable models were then converted into meshless point sets and underwent a 3d non rigid registration from the planning ct to the treatment cbct. the motion of deformable models during the registration was constrained by the global shape prior on the target surface during the deformation. the meshless formulation provided a convenient interface between deformable models and the image feature based registration method. the final registered deformable models in the cbct domain were further refined using the interaction between objects and other available image features. the segmentation results for 15 data sets has been included in the validation study, compared with manual segmentations by a radiation oncologist. the automatic segmentation results achieved a satisfactory convergence with manual segmentations and met the speed requirement for on line igrt.
interventional 4-d motion estimation and reconstruction of cardiac vasculature without motion periodicity assumption. anatomical and functional information of cardiac vasculature is a key component of future developments in the field of interventional cardiology. with the technology of c-arm ct it is possible to reconstruct intraprocedural 3-d images from angiographic projection data. current approaches attempt to add the temporal dimension (4-d) by ecg-gating in order to distinct physical states of the heart. this model assumes that the heart motion is periodic. however, frequently arrhytmic heart signals are observed in a clinical environment. in addition breathing motion can still occur. we present a reconstruction method based on a 4-d time-continuous motion field which is parameterized by the acquisition time and not the quasi-periodic heart phase. the output of our method is twofold. it provides a motion compensated 3-d reconstruction (anatomic information) and a motion field (functional information). in a physical phantom experiment a vessel of size 3.08 mm undergoing a non-periodic motion was reconstructed. the resulting diameters were 3.42 mm and 1.85 mm assuming non-periodic and periodic motion, respectively. further, for two clinical cases (coronary arteries and coronary sinus) it is demonstrated that the presented algorithm outperforms periodic approaches and is able to handle realistic irregular heart motion.
toward video-based navigation for endoscopic endonasal skull base surgery. endoscopic endonasal skull base surgery (esbs) requires high accuracy to ensure safe navigation of the critical anatomy at the anterior skull base. current navigation systems provide approximately 2mm accuracy. this level of registration error is due in part from the indirect nature of tracking used. we propose a method to directly track the position of the endoscope using video data. our method first reconstructs image feature points from video in 3d, and then registers the reconstructed point cloud to pre-operative data (e.g. ct/mri). after the initial registration, the system tracks image features and maintains the 2d-3d correspondence of image features and 3d locations. these data are then used to update the current camera pose. we present registration results within 1mm, which matches the accuracy of our validation framework.
a dynamical shape prior for lv segmentation from rt3d echocardiography. real-time three-dimensional (rt3d) echocardiography is the newest generation of three-dimensional (3-d) echocardiography. segmentation of rt3d echocardiographic images is essential for determining many important diagnostic parameters. in cardiac imaging, since the heart is a moving organ, prior knowledge regarding its shape and motion patterns becomes an important component for the segmentation task. however, most previous cardiac models are either static models (sm), which neglect the temporal coherence of a cardiac sequence or generic dynamical models (gdm), which neglect the inter-subject variability of cardiac motion. in this paper, we present a subject-specific dynamical model (ssdm) which simultaneously handles inter-subject variability and cardiac dynamics (intra-subject variability). it can progressively predict the shape and motion patterns of a new sequence at the current frame based on the shapes observed in the past frames. the incorporation of this ssdm into the segmentation process is formulated in a recursive bayesian framework. this results in a segmentation of each frame based on the intensity information of the current frame, as well as on the prediction from the previous frames. quantitative results on 15 rt3d echocardiographic sequences show that automatic segmentation with ssdm is superior to that of either sm or gdm, and is comparable to manual segmentation.
dynamic active constraints for hyper-redundant flexible robots. in robot-assisted procedures, the surgeon's ability can be enhanced by navigation guidance through the use of virtual fixtures or active constraints. this paper presents a real-time modeling scheme for dynamic active constraints with fast and simple mesh adaptation under cardiac deformation and changes in anatomic structure. a smooth tubular pathway is constructed which provides assistance for a flexible hyper-redundant robot to circumnavigate the heart with the aim of undertaking bilateral pulmonary vein isolation as part of a modified maze procedure for the treatment of debilitating arrhythmia and atrial fibrillation. in contrast to existing approaches, the method incorporates detailed geometrical constraints with explicit manipulation margins of the forbidden region for an entire articulated surgical instrument, rather than just the end-effector itself. detailed experimental validation is conducted to demonstrate the speed and accuracy of the instrument navigation with and without the use of the proposed dynamic constraints.
targeting accuracy under model-to-subject misalignments in model-guided cardiac surgery. in image-guided interventions, anatomical models of organs are often generated from pre-operative images and further employed in planning and guiding therapeutic procedures. however, the accuracy of these models, along with their registration to the subject are crucial for successful therapy delivery. these factors are amplified when manipulating soft tissue undergoing large deformations, such as the heart. when used in guiding beating-heart procedures, pre-operative models may not be sufficient for guidance and they are often complemented with real-time, intra-operative cardiac imaging. here we demonstrate via in vitro endocardial "therapy" that ultrasound-enhanced model-guided navigation provides sufficient guidance to preserve a clinically-desired targeting accuracy of under 3 mm independently of the model-to-subject misregistrations. these results emphasize the direct benefit of integrating real-time imaging within intra-operative visualization environments considering that model-to-subject misalignments are often encountered clinically.