nils-herrmann/clean_scirepeval huggingface dataset:
An automatic branch and stenoses detection in computed tomography angiography

In this work, we present an automatic branch and stenoses detection method that is capable of detecting all types of plaques in Computed Tomography Angiography (CTA) modality. Our method is based on the vessel extraction algorithm we proposed in , and detects branches and stenoses in a very fast way. We demonstrate the performance of our branch detection method on 3 complex tubular structured synthetic datasets for quantitative validation. Additionally, we show the preliminary results of stenoses detection algorithm on 11 CTA volumes, which are qualitatively evaluated by a cardiologist expert.
A vertex-based representation of objects in an image

Novel polygon evolution models are introduced in this paper for capturing polygonal object boundaries in images which have one or more objects that have statistically different distributions on the intensity values. The key idea in our approach is to design evolution equations for vertices of a polygon that integrate both local and global image characteristics. Our method naturally provides an efficient representation of an object through a few number of vertices, which also leads to a significant amount of compression of image content. This methodology can effectively be used in the context of MPEG-7. We also propose usage of the Jensen-Shannon criterion as an information measure between the densities of regions of an image to capture more general statistical characteristics of the data.
Cerebral vessel classification with convolutional neural networks

Analysing brain magnetic resonance angiography (MRA) images is important for detecting arteriovenous malformations and aneurysms. To detect these diseases, extracting the vessel structure in the image can be seen as a first step. In this paper, it was aimed to classify the cubic image parts obtained from brain MRA images according to whether they belong to vein structure or not. For this purpose, a 9 layers deep convolutional neural network (CNN) architecture is designed. With the model trained using this architecture, 85% accuracy was obtained in the classification performed on the test data.
Semi-Automatic 3-D Segmentation of Anatomical Structures of Brain MRI Volumes using Graph Cuts

We present a semi-automatic segmentation technique of the anatomical structures of the brain: cerebrum, cerebellum, and brain stem. The method uses graph cuts segmentation with an anatomic template for initialization. First, a skull stripping procedure is applied to remove non-brain tissues. Then, the segmentation is done hierarchically by first, extracting first the cerebrum from the brain, and then from the remaining volume the cerebellum and the brain stem are separated. This method is fast and can separate different anatomical structures of the brain in spite of weak boundaries. We describe our approach and present experimental results demonstrating its usefulness.
Concordance between computer-based neuroimaging findings and expert assessments in dementia grading

We present computer-based techniques for the measurement, detection and quantification of dementia related neuroimaging findings. The three focal points of these are: brain atrophy, lateral ventricle enlargement, and white matter hyperintensity (WMH) lesion detection and quantification. Research has shown that pathological changes in these three areas have strong correlations with dementia. Our proposed methods seek to provide consistent and accurate measures of these pathologies by applying advanced image processing techniques to brain MRIs. The results are evaluated by investigating correlations with experts' grades. We report correlations of up to 0.79, 0.70 and -0.24 for hemispheric cortical atrophy, central sulcus width, and central sulcus depth, respectively. For lateral ventricle (LV) enlargement, we obtain an LV-to-brain ratio correlation of up to 0.81 among other measurements. We automatically segment and quantify periventricular WMH lesions by adaptive thresholding and 3D connectivity analysis on fluid attenuation inversion recovery MRIs. A mean Dice score of 0.83 (standard deviation is 0.06), and a correlation score of up to 0.84 is obtained.
Algorithms for stochastic approximations of curvature flows

Curvature flows have been extensively considered from a deterministic point of view. They have been shown to be useful for a number of applications including crystal growth, flame propagation, and computer vision. In some previous work G. Ben-Arous et al. (2002), we have described a random particle system, evolving on the discretized unit circle, whose profile converges toward the Gauss-Minkowsky transformation of solutions of curve shortening flows initiated by convex curves. The present note shows that this theory may be implemented as a new way of evolving curves and as a possible alternative to level set methods.
REGISTRATION OF ULTRASOUND IMAGES USING AN INFORMATION-THEORETIC FEATURE DETECTOR

In this paper, we present a new method for ultrasound image registration. For each image to be registered, our method first applies an ultrasound-specific information-theoretic feature detector, which is based on statistical modeling of speckle and provides a feature image that robustly delineates important edges in the image. These feature images are then registered using differential equations, the solution of which provides a locally optimal transformation that brings the images into alignment. We describe our method and present experimental results demonstrating its effectiveness, particularly for low contrast, speckled images. Furthermore, we compare our method to standard gradient-based techniques, which we show are more susceptible to misregistration.
Anatomically-Aware, Automatic, and Fast Registration of 3D Ear Impression Models

We present a registration framework based on feature points of anatomical, 3D shapes represented in the point cloud domain. Anatomical information is utilized throughout the complete registration process. The surfaces, which in this paper are ear impression models, are considered to be similar in the way that they possess the same anatomical regions but with varying geometry. First, in a shape analysis step, features of important anatomical regions (such as canal, aperture, and concha) are extracted automatically. Next these features are used in ordinary differential equations that update rigid registration parameters between two sets of feature points. For refinement of the results, the GCP algorithm is applied. Through our experiments, we demonstrate our technique's success in surface registration through registration of key anatomical regions of human ear impressions. Furthermore, we show that the proposed method achieves higher accuracy and faster performance than the standard GCP registration algorithm.
Automatic registration of follow-up brain MRI scans

Registration of multiple medical images is required for analysis of both pathological and normal anatomical human body structures. In this study, we defined and automatically detected some anatomical landmarks which are considered to be unaffected from brain deformation such as symmetry axis of brain, nose tips, eye ball centers, and location of center of masses of certain slices. Registration is performed using the physical coordinates of automatically detected landmarks. Registration mapping is assumed to be an affine mapping and transformation matrix is formed using a least squares solution. Registered image is obtained using the estimated transformation matrix and trilinear interpolation. Algorithm performance is demonstrated on both MR image volumes deformed synthetically with a known transformation matrix for validation and baseline and follow-up MRI volumes with unknown transformations.
Inpainting by deep autoencoders using an advisor network

In this study, we tried to find a solution for inpainting problem using deep convolutional autoencoders. A new training approach has been proposed as an alternative to the Generative Adversarial Networks. The neural network that designed for inpainting takes an image, which the certain part of its center is extracted, as an input then it attempts to fill the blank region. During the training phase, a distinct deep convolutional neural network is used and it is called Advisor Network. We show that the features extracted from intermediate layers of the Advisor Network, which is trained on a different dataset for classification, improves the performance of the autoencoder.
Semi-Automatic Lymph Node Segmentation in LN-MRI

Accurate staging of nodal cancer still relies on surgical exploration because many primary malignancies spread via lymphatic dissemination. The purpose of this study was to utilize nanoparticle-enhanced lymphotropic magnetic resonance imaging (LN-MRI) to explore semi-automated noninvasive nodal cancer staging. We present a joint image segmentation and registration approach, which makes use of the problem specific information to increase the robustness of the algorithm to noise and weak contrast often observed in medical imaging applications. The effectiveness of the approach is demonstrated with a given lymph node segmentation problem in post-contrast pelvic MRI sequences.
Restoration of error-diffused images using projection onto convex sets

A novel inverse halftoning method is proposed to restore a continuous tone image from a given half-tone image. A set theoretic formulation is used where three sets are defined using the prior information about the problem. A new space-domain projection is introduced assuming the halftoning is performed using error diffusion, and the error diffusion filter kernel is known. The space-domain, frequency-domain, and space-scale domain projections are used alternately to obtain a feasible solution for the inverse halftoning problem which does not have a unique solution.
Shape-Driven Segmentation of the Arterial Wall in Intravascular Ultrasound Images

Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3-D reconstruction, and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior, we utilize an intensity prior through a nonparametric probability-density-based image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach.
Inter-hemispheric atrophy better correlates with expert ratings than hemispheric cortical atrophy

Brain atrophy is one of the parameters considered by experts for rating dementia from neuro-imaging findings. Research efforts have focused on measuring brain atrophy from images through experts' visual assessment or computer-based approaches. However, agreement between the visual assessment and computer-based measurement of atrophy is not yet investigated. Accordingly, this paper presents an automated method for cerebral atrophy assessment from cross-sectional MRI through hemispheric volume loss. For this purpose, the proposed method quantifies inter-hemispheric distance, and the distance between the cranium and the brain parenchyma, separately. Discriminative power of the proposed method is evaluated on two datasets with experts' visual gradings: Yue et al.'s data and a newly created reference dataset. Results show that inter-hemispheric atrophy better correlates with visual grades, and inconsistencies at the skull-stripping or brain tissue extraction steps can degrade the agreement between computer-based measurements and expert gradings.
Active polyhedron: surface evolution theory applied to deformable meshes

This paper presents a novel 3D deformable surface that we call an active polyhedron. Rooted in surface evolution theory, an active polyhedron is a polyhedral surface whose vertices deform to minimize a regional and/or boundary-based energy functional. Unlike continuous active surface models, the vertex motion of an active polyhedron is computed by integrating speed terms over polygonal faces of the surface. The resulting ordinary differential equations (ODEs) provide improved robustness to noise and allow for larger time steps compared to continuous active surfaces implemented with level set methods. We describe an electrostatic regularization technique that achieves global regularization while better preserving sharper local features. Experimental results demonstrate the effectiveness of an active polyhedron in solving segmentation problems as well as surface reconstruction from unorganized points.
Feature-preserving flows: a stochastic differential equation's view

Evolution equations have proven to be useful in tracking fine to coarse features in a single level curve and/or in an image. We give a stochastic insight to a specific evolution equation, namely the geometric heat equation, and subsequently use this insight to develop a class of feature-driven diffusions. A progressive smoothing along desired features of a level curve is aimed at overcoming effects of noisy environment during feature extraction and denoising applications.
Coupled Nonparametric Shape and Moment-Based Intershape Pose Priors for Multiple Basal Ganglia Structure Segmentation

This paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and intershape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance images. We present a set of 2-D and 3-D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentation methods and demonstrate the improvements provided by our approach in terms of segmentation accuracy.
A Sobolev-type metric for polar active contours

Polar object representations have proven to be a powerful shape model for many medical as well as other computer vision applications, such as interactive image segmentation or tracking. Inspired by recent work on Sobolev active contours we derive a Sobolev-type function space for polar curves. This so-called polar space is endowed with a metric that allows us to favor origin translations and scale changes over smooth deformations of the curve. Moreover, the resulting curve flow inherits the coarse-to-fine behavior of Sobolev active contours and is thus very robust to local minima. These properties make the resulting polar active contours a powerful segmentation tool for many medical applications, such as cross-sectional vessel segmentation, aneurysm analysis, or cell tracking.
A Higher-Order Tensor Vessel Tractography for Segmentation of Vascular Structures

A new vascular structure segmentation method, which is based on a cylindrical flux-based higher order tensor (HOT), is presented. On a vessel structure, the HOT naturally models branching points, which create challenges for vessel segmentation algorithms. In a general linear HOT model embedded in 3D, one has to work with an even order tensor due to an enforced antipodal-symmetry on the unit sphere. However, in scenarios such as in a bifurcation, the antipodally-symmetric tensor embedded in 3D will not be useful. In order to overcome that limitation, we embed the tensor in 4D and obtain a structure that can model asymmetric junction scenarios. During construction of a higher order tensor (e.g. third or fourth order) in 4D, the orientation vectors lie on the unit 3-sphere, in contrast to the unit 2-sphere in 3D tensor modeling. This 4D tensor is exploited in a seed-based vessel segmentation algorithm, where the principal directions of the 4D HOT is obtained by decomposition, and used in a HOT tractography approach. We demonstrate quantitative validation of the proposed algorithm on both synthetic complex tubular structures as well as real cerebral vasculature in Magnetic Resonance Angiography (MRA) datasets and coronary arteries from Computed Tomography Angiography (CTA) volumes.
Interacting Active Rectangles for Estimation of Intervertebral Disk Orientation

This paper presents a fast and efficient method to determine intervertebral disk orientation in a magnetic resonance (MR) image of the spine. The algorithm originates from active contour theory and enforces a shape constraint to avoid leaks through weak or non-existent boundaries. The method represents a vertebra as a rectangle, modeled as a semi-affine transformation applied to the unit square. A regional flow integrated along the rectangle's perimeter updates the rectangle's transformation to achieve the segmentation. Further constraints are added so that adjacent rectangles have similar orientation and scale. The orientation of a disk is then inferred from its adjacent vertebrae. Experiments show that the method is fast and effective in detecting the correct intervertebral disk orientation, which is used for transverse image planning
3-D statistical shape modeling and application to prototyping of hearing aids

In this paper, we present a 3D shape modeling method using a statistical shape analysis framework, for design of implants and prosthetic devices customized to a patient's anatomy. We present a novel method to learn the relationship between two classes of shapes, which are related by certain operations or transformation. The two associated shape classes are represented in a lower dimensional manifold, and the reduced set of parameters obtained in this subspace is utilized in an estimation, which is exemplified by a multivariate regression in this paper. We demonstrate our method with a felicitous application to estimation of customized hearing aid devices.
Plant image retrieval using color and texture features

An application of content-based image retrieval is proposed for identifying plants, along with a preliminary implementation. The system takes a plant image as input and finds the matching plant from a plant image database and is intended to provide users a simple method to locate information about their house plants. Max-flow min-cut technique is used as the image segmentation method to extract the general structure of the plant. Various color and texture features extracted from the segmented plant region are used in matching images to the database. Results show that for 60% of the queries, the correct plant image is retrieved among the top-10 results, using a small database of 188 images.
Fast incorporation of optical flow into active polygons

In this paper, we first reconsider, in a different light, the addition of a prediction step to active contour-based visual tracking using an optical flow and clarify the local computation of the latter along the boundaries of continuous active contours with appropriate regularizers. We subsequently detail our contribution of computing an optical flow-based prediction step directly from the parameters of an active polygon, and of exploiting it in object tracking. This is in contrast to an explicitly separate computation of the optical flow and its ad hoc application. It also provides an inherent regularization effect resulting from integrating measurements along polygon edges. As a result, we completely avoid the need of adding ad hoc regularizing terms to the optical flow computations, and the inevitably arbitrary associated weighting parameters. This direct integration of optical flow into the active polygon framework distinguishes this technique from most previous contour-based approaches, where regularization terms are theoretically, as well as practically, essential. The greater robustness and speed due to a reduced number of parameters of this technique are additional and appealing features.
A Variational Approach to the Evolution of Radial Basis Functions for Image Segmentation

In this paper we derive differential equations for evolving radial basis functions (RBFs) to solve segmentation problems. The differential equations result from applying variational calculus to energy functionals designed for image segmentation. Our methodology supports evolution of all parameters of each RBF, including its position, weight, orientation, and anisotropy, if present. Our framework is general and can be applied to numerous RBF interpolants. The resulting approach retains some of the ideal features of implicit active contours, like topological adaptivity, while requiring low storage overhead due to the sparsity of our representation, which is an unstructured list of RBFs. We present the theory behind our technique and demonstrate its usefulness for image segmentation.
Ultrasound-Specific Segmentation via Decorrelation and Statistical Region-Based Active Contours

Segmentation of ultrasound images is often a very challenging task due to speckle noise that contaminates the image. It is well known that speckle noise exhibits an asymmetric distribution as well as significant spatial correlation. Since these attributes can be difficult to model, many previous ultrasound segmentation methods oversimplify the problem by assuming that the noise is white and/or Gaussian, resulting in generic approaches that are actually more suitable to MR and X-ray segmentation than ultrasound. Unlike these methods, in this paper we present an ultrasound-specific segmentation approach that first decorrelates the image, and then performs segmentation on the whitened result using statistical region-based active contours. In particular, we design a gradient ascent flow that evolves the active contours to maximize a log likelihood functional based on the Fisher-Tippett distribution. We present experimental results that demonstrate the effectiveness of our method.
Coupled nonparametric shape priors for segmentation of multiple basal ganglia structures

This paper presents a new method for multiple structure segmentation, using a maximum a posteriori (MAP) estimation framework, based on prior shape densities involving nonparametric multivariate kernel density estimation of multiple shapes. Our method is motivated by the observation that neighboring or coupling structures in medical images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our technique allows simultaneous segmentation of multiple objects, where highly contrasted, easy-to-segment structures can help improve the segmentation of weakly contrasted objects. We demonstrate the effectiveness of our method on both synthetic images and real magnetic resonance images (MRI) for segmentation of basal ganglia structures.