nils-herrmann/clean_scirepeval huggingface dataset:
Motion Analysis of Endovascular Stent-Grafts by MDL Based Registration

The endovascular repair of a traumatic rupture of the thoracic aorta - that would otherwise lead to the death of the patient - is performed by delivering a stent-graft into the vessel at the rupture location. The age range of the affected patients is large and the stent-graft will stay in the body for the remaining life. The technique is relatively new, and no experience with regard to long-term effects, and durability exists. To predict long-term complications, such as ruptures or destructive interactions with surrounding tissue during the life of the patient, it is important to understand the - rather intense and constant - movement of the stent- graft during the cardiac cycle. A computed tomography with heart gating (gated CT) acquires sequences that show the region of the stent-graft at different time points. We analyze the motion of stent-grafts with a model based approach. Stent-grafts are represented as sparse sets of axis points extracted from the gated CT, and motion patterns are captured by a minimum description length based group-wise registration of the stent-graft at different time points. No parameterization or a priori definition of the topology is necessary, and highly variable elasticity properties in the data volume can by accounted for by the sparse statistical model, that captures correlations and motion components of the stent-graft. We report results for deformation models and registration accuracy for 5 patients.
Shape grammar parsing via Reinforcement Learning

We address shape grammar parsing for facade segmentation using Reinforcement Learning (RL). Shape parsing entails simultaneously optimizing the geometry and the topology (e.g. number of floors) of the facade, so as to optimize the fit of the predicted shape with the responses of pixel-level 'terminal detectors'. We formulate this problem in terms of a Hierarchical Markov Decision Process, by employing a recursive binary split grammar. This allows us to use RL to efficiently find the optimal parse of a given facade in terms of our shape grammar. Building on the RL paradigm, we exploit state aggregation to speedup computation, and introduce image-driven exploration in RL to accelerate convergence. We achieve state-of-the-art results on facade parsing, with a significant speed-up compared to existing methods, and substantial robustness to initial conditions. We demonstrate that the method can also be applied to interactive segmentation, and to a broad variety of architectural styles.
Image Renaissance Using Discrete Optimization

In this paper we propose a novel technique to image completion that addresses image renaissance through a graph-based matching process. To this end, a number of candidate seeds with content similar to the one of the area to be inpainted are considered. They are selected through a particle filter method and then positioned over the missing area. Markov random fields are used to formalize inpainting as a labeling estimation problem while a combinatorial approach is used to recover the optimal partition of patches that completes the missing area with the alpha-expansion process. Promising results in image and texture completion demonstrate the potentials of the proposed method
Implicit planar and in-plane deformable mapping in medical images through high order graphs

In this paper we propose a novel method based on discrete optimization of high order graphs, to perform deformable slice-to-volume registration of 2D images and 3D volumes. To this end, a 2D grid superimposed to the image is considered with control points deforming in 3D and their deformations corresponding to the label space. Geometrical consistency (unique plane selection) and deformation smoothness (in-plane deformations) as well as image similarity (visual matching) are encoded in different third order cliques. The proposed formulation is optimized through its mapping to a factor graph using conventional graph optimization methods. A dataset composed of 2D slices and 3D MRI volumes of the heart was used to evaluate its accuracy leading to very promising results.
A variational approach for the segmentation of the left ventricle in MR cardiac images

This paper proposes a new front propagation method to segment MR cardiac images. This framework is based on the geodesic active region model, refers to a coupled propagation of two curves (inner and outer cardiac contours) and integrates boundary and region-based segmentation modules. The boundary information is introduced to the objective function using the gradient vector flow framework while the region information using continuous probability density functions. The defined objective function is minimised using a gradient descent method and the obtained motion equations are implemented using a level set approach. A recently introduced numerical approximation scheme with fast convergence rate and stable behavior is used to implement the level set motion equations. Finally, according to the application the propagations of the level set contours are coupled using their relative distances. Encouraging experimental results are provided using real data.
Image Denoising Based on Adapted Dictionary Computation

This paper introduces a new denoising technique that consists in recovering the image using a filtering function adapted to the image content. The definition of such a function relies on the computation of similarity between pixels of a given neighborhood. Our contribution consists in the definition of a new similarity criterion which is more robust to noise. This measure is computed from a dictionary that is adapted to image content. The projection of the image content to this subspace are used then to define a metric between a pixel and the neighborhood ones. Very promising experimental results show the potential of our approach.
Optimal Estimation of Diffusion in DW-MRI by High-Order MRF-Based Joint Deformable Registration and Diffusion Modeling

Over the last years, the apparent diffusion coefficient (ADC), computed from diffusion-weighted magnetic resonance (DW-MR) images, has become an important imaging biomarker for evaluating and managing patients with neoplastic or cerebrovascular disease. Standard methods for the calculation of ADC ignore the presence of noise and motion between successive (in time) DW-MR images acquired by changing the b-value. In order to accurately quantify the diffusion process during image acquisition, we introduce a method based on a high-order Markov Random Field (MRF) formulation that jointly registers the DWMR images and models the spatiotemporal diffusion. Spatial smoothness on the ADC map, as well as spatiotemporal deformation smoothness, is imposed towards producing anatomically meaningful representations. The high-order dependencies in our MRF model are handled through Dual Decomposition. Performance of registration is compared to a state-of-the art registration approach in terms of obtained fitting error of the diffusion model in the core of the tumor. Preliminary results reveal a marginally better performance of our method when compared against the standard ADC map used in clinical practice, which indicates its potential as a means for extracting imaging biomarkers.
From Uncertainties to Statistical Model Building and Segmentation of the Left Ventricle

Reliable segmentation of the left ventricle is a long sought objective in medical imaging for automatic retrieval of anatomical and pathological measurements and detection of malfunctions. In this paper, we propose a novel model-constrained approach to address this task. The method is based on an implicit representation of the shape model used in a shape registration framework with a Thin Plate Spline transform to retrieve possible deformations. The main innovation of our approach resides in the use of uncertainties defined on the registered shape to augment the training set and improve the robustness of the statistical deformable model. We use ICA to reduce the dimensionality of the space of deformations and provide a good separation of the different deformable parts of the heart. Furthermore the estimation of uncertainties is also introduced in the segmentation process which is addressed in a variational framework where prior knowledge and visual support are considered. The method lead to very promising qualitative and quantitative experimental results in CT.
Knee reconstruction through efficient linear programming

In this paper we propose a novel method to recover the 3D shape of the knee and of the prosthesis for the patients that have undergone total knee replacement surgery. We address this using a set of 2D X-Ray images of the knee taken from different viewpoints around it. The problem is then casted to a multiview stereo reconstruction problem and we consider an energy-minimization approach to recover the bone structure. This function is designed to recover a 3D surface which minimizes image and shape-based terms. The cost related to data is defined through the projections of the 3D surface onto 2D image planes while the prior-knowledge is introduced using a 3D model of the knee. The use of Markov Random Fileds as well as recent advances in discrete optimization and in particular the state-of-the-art Fast PD optimization algorithm is considered towards optimal reconstruction. Promising results demonstrate the potentials of our method.
Geodesic active regions for motion estimation and tracking

This paper proposes a new front propagation method to deal accurately with the challenging problem of tracking non-rigid moving objects. This is obtained by employing a geodesic active region model where the designed objective function is composed of boundary and region-based terms and optimizes the curve position with respect to motion and intensity properties. The main novelty of our approach is that we deal with the motion estimation (linear models are assumed) and the tracking problem simultaneously. In other words, the optimization problem contains a coupled set of unknown variables; the curve position and the corresponding motion model. The designed objective function is minimized using a gradient descent method; the curve is propagated towards the object boundaries under the influence of boundary, intensity and motion-based forces using a PDE, while given the curve position an incremental analytical solution is obtained for the motion model. Besides, this PDE is implemented using a level set approach where topological changes are naturally handled. Very promising experimental results are provided using real video sequences.
Linear image registration through MRF optimization

We propose a Markov Random Field formulation for the linear image registration problem. Transformation parameters are represented by nodes in a fully connected graph where the edges model pairwise dependencies. Parameter estimation is then solved through iterative discrete labeling and discrete optimization while a label space refinement strategy is employed to achieve sub-millimeter accuracy. Our framework can encode any similarity measure, allows for automatic reduction of the degrees of freedom by simple changes on the MRF topology, and is robust to initialization. Promising results on real data and random studies demonstrate the potential of our approach.
(Hyper)-Graphs Inference through Convex Relaxations and Move Making Algorithms: Contributions and Applications in Artificial Vision

Computational visual perception seeks to reproduce human visionthrough the combination of visual sensors, artificial intelligence andcomputing. To this end, computer vision tasks are often reformulatedas mathematical inference problems where the objective is to determinethe set of parameters corresponding to the lowest potential of a taskspecificobjective function. Graphical models have been the most popularformulation in the field over the past two decades where the problemis viewed as a discrete assignment labeling one. Modularity, scalabilityand portability are the main strengths of these methods which oncecombined with efficient inference algorithms they could lead to state ofthe art results. In this tutorial we focus on the inference component ofthe problem and in particular we discuss in a systematic manner themost commonly used optimization principles in the context of graphicalmodels. Our study concerns inference over low rank models interactionsbetween variables are constrained to pairs as well as higher orderones arbitrary set of variables determine hyper-cliques on which constraintsare introduced and seeks a concise, self-contained presentationof prior art as well as the presentation of the current state of the artmethods in the field.
Automatic detection of liver tumors

Tumor detection in CT liver images is a challenging task. The nature of tumor has a direct effect on the number of voxels being contaminated, as well as on the changes in the observed CT scan. In order to deal with this challenge, in this paper we propose the use of advanced non-linear machine learning techniques to determine the optimal features, as well as the hyperplane that use these features to separate tumoral voxels from voxels corresponding to healthy tissues. Very promising classification results using an important volume of clinically annotated data (86% sensitivity, 82% specificity) demonstrate the potentials of our approach.
Bioluminescence enhancement through fusion of optical imaging and cinematic video flow

Optical imaging is an efficient mean to measure biological signal. However, it can suffer from low spatial and temporal resolution while animal deformable displacements could also degrade significantly the localization of the measurements. In this paper, we propose a novel approach to perform fusion of cinematic flow and optical imaging towards enhancement of the biological signal. To this end, fusion is reformulated as a population (all vs. all) registration problem where the two (being spatially aligned) signals are registered in time using the same deformation field. Implicit silhouette and landmark matching are considered for the cinematic images and are combined with global statistical congealing-type measurements of the optical one. The problem is reformulated using a discrete MRF, where optical imaging costs are expressed in singleton (global) potentials, while smoothness constraints as well as cinematic measurements through pair-wise potentials. Promising experimental results demonstrate the potentials of our approach.
A PDE-based level-set approach for detection and tracking of moving objects

This paper presents a framework for detecting and tracking moving objects in a sequence of images. Using a statistical approach, where the inter-frame difference is modeled by a mixture of two Laplacian or Gaussian distributions, and an energy minimization based approach, we reformulate the motion detection and tracking problem as a front propagation problem. The Euler-Lagrange equation of the designed energy functional is first derived and the flow minimizing the energy is then obtained. Following the work by Caselles et al. (1995) and Malladi et al. (1995), the contours to be detected and tracked are modeled as geodesic active contours evolving toward the minimum of the designed energy, under the influence of internal and external image dependent forces. Using the level set formulation scheme of Osher and Sethian (1988), complex curves can be detected and tracked and topological changes for the evolving curves are naturally managed. To reduce the computational cost required by a direct implementation, of the formulation scheme of Osher and Sethian (1988), a new approach exploiting aspects from the classical narrow band and fast marching methods is proposed and favorably compared to them. In order to further reduce the CPU time, a multi-scale approach has also been considered. Very promising experimental results are provided using real video sequences.
Efficient and Automated Multimodal Satellite Data Registration through MRFs and Linear Programming

The accurate and automated registration of multimodal remote sensing data is of fundamental importance for numerous emerging geospatial environmental and engineering applications. However, the registration of very large multimodal, multitemporal, with different spatial resolutions data is, still, an open matter. To this end, we propose a generic and automated registration framework based on Markov Random Fields (MRFs) and efficient linear programming. The discrete optimization setting along with the introduced data-specific energy terms form a modular approach with respect to the similarity criterion allowing to fully exploit the spectral properties of multimodal remote sensing datasets. The proposed approach was validated both qualitatively and quantitatively demonstrating its potentials on very large (more than 100M pixels) multitemporal remote sensing datasets. In particular, in terms of spatial accuracy the geometry of the optical and radar data has been recovered with displacement errors of less than 2 and 3 pixels, respectively. In terms of computational efficiency the optical data term can converge after 7-8 minutes, while the radar data term after less than 15 minutes.
Registration with Uncertainties and Statistical Modeling of Shapes with Variable Metric Kernels

Registration and modeling of shapes are two important problems in computer vision and pattern recognition. Despite enormous progress made over the past decade, these problems are still open. In this paper, we advance the state of the art in both directions. First we consider an efficient registration method that aims to recover a one-to-one correspondence between shapes and introduce measures of uncertainties driven from the data which explain the local support of the recovered transformations. To this end, a free form deformation is used to describe the deformation model. The transformation is combined with an objective function defined in the space of implicit functions used to represent shapes. Once the registration parameters have been recovered, we introduce a novel technique for model building and statistical interpretation of the training examples based on a variable bandwidth kernel approach. The support on the kernels varies spatially and is determined according to the uncertainties of the registration process. Such a technique introduces the ability to account for potential registration errors in the model. Hand-written character recognition and knowledge-based object extraction in medical images are examples of applications that demonstrate the potentials of the proposed framework.
Local atlas selection for discrete multi-atlas segmentation

Multi-atlas segmentation is commonly performed in two separate steps: i) multiple pairwise registrations, and ii) fusion of the deformed segmentation masks towards labeling objects of interest. In this paper we propose an approach for integrated volume segmentation through multi-atlas registration. To tackle this problem, we opt for a graphical model where registration and segmentation nodes are coupled. The aim is to recover simultaneously all atlas deformations along with selection masks quantifying the participation of each atlas per segmentation voxel. The above is modeled using a pairwise graphical model where deformation and segmentation variables are modeled explicitly. A sequential optimization relaxation is proposed for efficient inference. Promising performance is reported on the IBSR dataset when comparing to majority voting and local appearance-based weighted voting.
Classification of tensors and fiber tracts using Mercer-kernels encoding soft probabilistic spatial and diffusion information

In this paper, we present a kernel-based approach to the clustering of diffusion tensors and fiber tracts. We propose to use a Mercer kernel over the tensor space where both spatial and diffusion information are taken into account. This kernel highlights implicitly the connectivity along fiber tracts. Tensor segmentation is performed using kernel-PCA compounded with a landmark-Isomap embedding and k-means clustering. Based on a soft fiber representation, we extend the tensor kernel to deal with fiber tracts using the multi-instance kernel that reflects not only interactions between points along fiber tracts, but also the interactions between diffusion tensors. This unsupervised method is further extended by way of an atlas-based registration of diffusion-free images, followed by a classification of fibers based on nonlinear kernel Support Vector Machines (SVMs). Promising experimental results of tensor and fiber classification of the human skeletal muscle over a significant set of healthy and diseased subjects demonstrate the potential of our approach.
Direct 3D-reconstruction from a single 2D-image obtained through a coded-aperture

Reconstructions in emission tomography at 511 keV have to address insufficient amount of data due to poor performances of detectors at such energy. SPECT with classic collimators is generally not adapted to acquire such images, unless a large open-fraction collimator is considered. In this paper we propose a novel configuration for direct 3D-reconstruction from a single 2D-planar non-parallel projection, that is a challenging and ill-posed problem. First, a coded aperture and a 2D-detector are simulated to define a model of the imaging system and to generate synthetic data. In order to address the absence of sufficient data support, 3D-objects that are synthesized stand in the near- field region, at just a few centimetres from the surface of the detector. This configuration maximises the geometrical efficiency and consequently enhances the amount of data contained in the unique observed image. Secondly, an accelerated ML-EM algorithm is used to reconstruct 3D-objects. The basis of our approach reflects the assumption that the inner-structure of the transition matrix used for modelling the system affects significantly the behaviour of ML-EM algorithm and the whole reconstructed spatial distribution. The transition matrix depends directly on the geometry of the collimator. The impact of this geometry on the structure of the transition matrix is studied. This paper shows that a small-size detector is able to perform a 3D-reconstruction of a small object standing in the near-field domain, from a single 2D-image. Reconstructed objects exhibit a good spatial resolution : two 5 mm-diameter cylinders with a centre-to-centre distance of 6 mm are clearly separated. Cylindrical shapes are well-preserved, although depth direction is deformed. Promising simulations demonstrate the extreme potentials of our approach.
Variational methods and partial differential equations in cardiac image analysis

Cardio-vascular diseases is a major cause of deaths worldwide. Early diagnosis is quite often associated with more efficient treatment leading to a significant decrease of the mortality rate. In this paper, we present tools developed to process different cardiac modalities over the past years with aim to facilitate/automate computer aided diagnosis. In particular we present algorithms and advanced techniques for the segmentation of the left ventricle for magnetic resonance and echocardiographic imaging. Furthermore, to account for user interaction, we extent our method towards user-aided boundary delineation where the user has the ability to correct local discrepancies of automated results.