nils-herrmann/clean_scirepeval huggingface dataset:
High-speed operation of a 64-bit circular shift register

A 64-bit superconducting rapid single flux quantum (RSFQ) circular shift register (CSR) has been demonstrated to operate at clock frequencies up to 18 GHz. The CSR was designed with special attention to timing constraints, which are more severe for "recurrent" circuits (where data must circulate around closed feedback loops) than for systolic arrays where only local timing constraints are relevant. The longest recurrent data path ever demonstrated previously was only several stages long. The testing scheme demonstrated here is unique in that the data circulated up to 50 million times before verification.
An adaptive spread-spectrum data hiding technique for digital audio

In this paper we describe an application of spread spectrum techniques in audio data hiding for watermarking and steganography. The method is self-synchronizing, cover-dependent, and operates in the time domain. We use a special class of frequency-hop signal known as a Welch-Costas array. Welch-Costas arrays have the properties of range and Doppler resolution. This allows us to recover embedded data with a matched filter. We also demonstrate a special case of an adaptive method due to Su and Girod (2002).
Empirical physical modeling for bowed string instruments

The various components needed to define an empirical violin physical model are assembled and their interaction is detailed. We experimentally measure the mechanical admittance at the violin bridge using the impact hammer excitation method and the monopole radiativity in anechoic conditions. These are combined with a digital waveguide model computing the dynamical behavior of the bowed string. Theoretical bridge oscillations and sound radiation are defined by convolution of the bowed string physical model signals with impulse responses determined from experiment.
Thermometry using thermal activation of Josephson junctions at MilliKelvin temperatures

We use the thermal activation of Josephson junctions as a thermometer to investigate heat flow from a hot resistor at milliKelvin temperatures on a silicon chip used for superconducting qubit experiments. The experiments are compared to computer simulations and agree well. These results indicate that on-chip resistors can be used below a certain power level, but not above that level.
Polyphonic music transcription employing max-margin classification of spectrograhic features

In this paper we present a transcription method for polyphonic music. The short time Fourier transform is used first to decompose an acoustic signal into sonic partials in a time-frequency representation. In general the segmented partials exhibit distinguishable features if they originate from different “voices” in the polyphonic mix. We define feature vectors and utilize a max-margin classification algorithm to produce classification labels to serve as grouping cues, i.e., to decide which partials should be assigned to each voice. These classification labels are then used in statistical optimal grouping decisions and confidence levels are assigned to each decision. This classification algorithm shows promising results for the musical source separation.
High precision robust modeling of long room responses using wavelet transform

Modeling of a room impulse response (RIR) is required in many audio processing applications; however, this is challenging since room responses are usually long and complex in practice and drastically vary as the source and microphone locations change. In this paper, a subband multichannel modeling method is proposed, which is computationally efficient, precise, and robust against RIR variations. A dual-tree complex wavelet packet transform is utilized to decompose a multichannel RIR into aliasing-free subband signals, and low order adaptive Kautz filters are designed to model subband signals using the poles common to the RIR channels. A least-squares algorithm is introduced to efficiently estimate the common poles at each subband. Experimental results indicate that the proposed method accurately models long room responses, while exhibiting significant robustness against room response variations caused by changing the source and microphone locations.
Vessel Tractography Using an Intensity Based Tensor Model With Branch Detection

In this paper, we present a tubular structure segmentation method that utilizes a second order tensor constructed from directional intensity measurements, which is inspired from diffusion tensor image (DTI) modeling. The constructed anisotropic tensor which is fit inside a vessel drives the segmentation analogously to a tractography approach in DTI. Our model is initialized at a single seed point and is capable of capturing whole vessel trees by an automatic branch detection algorithm developed in the same framework. The centerline of the vessel as well as its thickness is extracted. Performance results within the Rotterdam Coronary Artery Algorithm Evaluation framework are provided for comparison with existing techniques. 96.4% average overlap with ground truth delineated by experts is obtained in addition to other measures reported in the paper. Moreover, we demonstrate further quantitative results over synthetic vascular datasets, and we provide quantitative experiments for branch detection on patient computed tomography angiography (CTA) volumes, as well as qualitative evaluations on the same CTA datasets, from visual scores by a cardiologist expert.
segmentation of multiple brain structures using coupled nonparametric shape priors

This paper presents a new approach for segmentation of multiple brain structures. We introduce a new coupled shape prior for neighboring structures in magnetic resonance images (MRI) for multi object segmentation problem, where the information obtained from images can not provide enough contrast or exact boundary. In segmentation of low contrasted brain structures we take the advantage of using prior information enforced by interaction between neighboring structures in a nonparametric estimation fashion. Using nonparametric density estimation of multiple shapes, we introduce the coupled shape prior information into the segmentation process which is based on active contour models. We demonstrate the effectiveness of our method on real magnetic resonance images in challenging segmentation scenarios where existing methods fail.
Elliptic fourier features of brain white matter pathways

Magnetic resonance imaging provides diffusion weighted images (DMRI), which non-invasively reconstruct the brain white matter pathways. DMRI is used to study brain white matter diseases as well as aid surgical planning. As localization of different white matter pathways surrounding a pathology is crucial for surgical planning, automatic extraction and classification of different anatomical white matter pathways pre-operatively becomes an important computational tool. In this paper, we propose a method for classification of brain white matter pathways based on 3D elliptic Fourier descriptors, which are extended from the 2D elliptic Fourier descriptors. We performed experiments and validation of the proposed method on a white matter atlas space and on real pathological cases.
Freeform shape clustering for customized design automation

Automation can provide significant performance improvements in digital manufacturing systems that customize shapes of implants and prosthetic devices to the anatomy of a patient. The challenge, however, lies in the ability of an automatic solution to adapt to anatomical variations of a given object category. This paper presents a hierarchical framework that generalizes the digital design of anatomical surface models in terms of a small number of prototypes. The latter are derived from the local shape information of constituent parts via shape matching and clustering and then associated with one operation that dictates how a shape undergoes modification. We demonstrate the proposed technique through application to typical hearing aid design operations with promising results.
A contour-based approach to 3D text labeling on triangulated surfaces

This paper presents a simple and efficient method of forming a 3D text label on a 3D triangulated surface. The label is formed by projecting the 2D contours that define the text silhouette onto the triangulated surface, forming 3D contour paths. Surface polygons upon which the 3D contour paths lie are retriangulated using a novel approach that forms a polyline defining the region outside the contour. This algorithm produces labeled 3D surfaces that conform to the specifications of the STL format, making them suitable for fabrication by a rapid prototyping machine. We demonstrate the effectiveness of the algorithm in forming flat and extruded labels on non-trivial surfaces.
A cerebral blood vessels segmentation method using a flux based second order tensor model

In this paper, we view the segmentation of cerebral blood vessels from Digital Subtraction Angiography (DSA) and Rotational Angiography (RA) problem from a tensor estimation and tractography perspective as in diffusion tensor imaging (DTI). We have developed a flux based multi-directional cylinder model that fits to a second-order tensor whose principal eigenvector represents the vessel's centerline. This anisotropic tensor inside the vessel drives the segmentation analogously to a tractography approach in DTI analysis starting from a seed point used as initialization.
Classification of brain tissues as lesion or healthy by 3D convolutional neural networks

In this paper, a three dimensional convolutional neural network based solution is proposed for classification of brain tissues as lesion or healthy in terms of ischemic stroke disease. Three dimensional data used in this work are obtained by magnetic resonance imaging technique. Proposed method is compared with traditional methods that are in the same category, via K-fold cross validation technique in terms of sensitivity, specificity and accuracy measures. In conclusion, it is obtained nearly 89% accuracy using our proposed method. Comparing this method with others, our proposed method is the best method.
Volumetric segmentation of multiple basal ganglia structures using nonparametric coupled shape and inter-shape pose priors

We present a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. Neighboring anatomical structures in the human brain 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 inter-shape (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 based on training data, 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 (MR) images and present a quantitative performance analysis. We compare our technique with existing methods and demonstrate the improvements it provides in terms of segmentation accuracy.
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients - manually annotated by up to four raters - and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.
Stochastic differential equations and geometric flows

In previous years, curve evolution, applied to a single contour or to the level sets of an image via partial differential equations, has emerged as an important tool in image processing and computer vision. Curve evolution techniques have been utilized in problems such as image smoothing, segmentation, and shape analysis. We give a local stochastic interpretation of the basic curve smoothing equation, the so called geometric heat equation, and show that this evolution amounts to a tangential diffusion movement of the particles along the contour. Moreover, assuming that a priori information about the shapes of objects in an image is known, we present modifications of the geometric heat equation designed to preserve certain features in these shapes while removing noise. We also show how these new flows may be applied to smooth noisy curves without destroying their larger scale features, in contrast to the original geometric heat flow which tends to circularize any closed curve.
In-vivo Optical Coherence Tomography image analysis

Optical Coherence Tomography (OCT) is a recent modality, which measures the intensity of back-reflected infrared light. The main advantage of OCT is its high resolution at the cost of a decreased penetration depth. OCT can differentiate between typical constituents of atherosclerotic plaques, such as lipid, calcium, and fibrous tissue better than intravascular ultrasound as shown by recent studies, also related to plaque vulnerability. Moreover, it has been shown that OCT provides monitoring of stents imaged in-vivo after percutaneous coronary interventions, which is important for quantification of in-stent neointimal hyperplasia. Image analysis problems using intra-coronary OCT pullbacks will be presented for study of atherosclerotic plaque composition and mainly follow-up of in-stent restenosis using in-vivo OCT images.
Efficient classification of scanned media using spatial statistics

We address the automatic classification of scanned input media in order to improve color calibration. Since scanner responses vary significantly according to the type of input, a media dependent color calibration for a scanner is desirable for accurately mapping scanner responses to a standard color space. To assist such media dependent calibration, we propose an efficient algorithm for automated classification of input media into four major classes corresponding to photographic, lithographic, xerographic and inkjet. Our technique exploits the strong correlation between the type of input medium and the spatial statistics of corresponding images, which may be observed in the scanned images. Adopting two spatial statistical measures of dispersion and periodicity and utilizing extensive training data, we determine well separated decision regions to classify the input medium with a high confidence level. Experimental results over an independent test data set validate the results.
An information-theoretic detector based scheme for registration of speckled medical images

Several studies dealt with medical ultrasound registration. Their similarity metrics relied on pixel-to-pixel intensity comparisons. Hence, they are not well suited to the case of speckled images. To better handle the speckle noise, our previous work proposed an information-theoretic feature detector-based registration approach. This work aims to extend it to the cases where the image speckle model is Rayleigh or normalized Fisher-Tippett distributed. Using speckle modeling based on these distributions, a speckle-specific information- theoretic feature detector is constructed and applied to provide feature images. Those feature images are then registered using differential equations, the solution of which provides a transformation to bring the images into alignment. Compared to standard gradient-based techniques, the experimental results demonstrate the effectiveness of our method, particularly for low contrast ultrasound images.
Information-Theoretic Feature Detection in Ultrasound Images

The detection of image features is an essential component of medical image processing, and has wide-ranging applications including adaptive filtering, segmentation, and registration. In this paper, we present an information-theoretic approach to feature detection in ultrasound images. Ultrasound images are corrupted by speckle noise, which is a disruptive random pattern that obscures the features of interest. Using theoretical probability density functions of the speckle intensity distributions, we derive analytic expressions that measure the distance between distributions taken from different regions in an ultrasound image and use these distances to detect features. We compare the technique to classic gradient-based feature detection methods
Guidewire tracking in x-ray videos of endovascular interventions

We present a novel method to track a guidewire in cardiac X-ray video. Using variational calculus, we derive differential equations that deform a spline, subject to intrinsic and extrinsic forces, so that it matches the image data, remains smooth, and preserves an a priori length. We analytically derive these equations from first principles, and show how they include tangential terms, which we include in our model. To address the poor contrast often observed in X-ray video, we propose using phase congruency as an image-based feature. Experimental results demonstrate the success of the method in tracking guidewires in low contrast X-ray video.
A convolutional neural networks oriented approach for voxel-based 3D object classification

In our work, 3D objects classification has been dealt with convolutional neural networks which is a common paradigm recently in image recognition. In the first phase of experiments, 3D models in ModelNet10 and ModelNet40 data sets were voxelized and scaled with certain parameters. Classical CNN and 3D Dense CNN architectures were designed for training the pre-processed data. In addition, the two trained CNNs were ensembled and the results of them were observed. A success rate of 95.37% achieved on ModelNet10 by using 3D dense CNN, a success rate of 91.24% achieved with ensemble of two CNNs on ModelNet40.
Adaptive reconstruction for vessel preservation in unenhanced MR angiography

The image quality of unenhanced magnetic resonance angiography, which images blood vessels without contrast agents, is limited by constraints related to scan time. To address this problem, techniques that undersample angiographic data and then apply regularized reconstructions are used. Conventional reconstructions employ regularization terms with uniform spatial weighting. Thus, they can yield improper suppression of aliasing artifacts and poor blood/background contrast. In this study, a reconstruction strategy is evaluated that applies spatially-adaptive regularization based on vessel maps obtained via a tractographic segmentation. This strategy is compared with conventional methods in terms of peak signal to noise ratio, structural similarity and contrast.
Coupled PDEs for non-rigid registration and segmentation

In this paper we present coupled partial differential equations (PDEs) for the problem of joint segmentation and registration. The registration component of the method estimates a deformation field between boundaries of two structures. The desired coupling comes from two PDEs that estimate a common surface through segmentation and its non-rigid registration with a target image. The solutions of these two PDEs both decrease the total energy of the surface, and therefore aid each other in finding a locally optimal solution. Our technique differs from recently popular joint segmentation and registration algorithms, all of which assume a rigid transformation among shapes. We present both the theory and results that demonstrate the effectiveness of the approach.
Vessel tractography for magnetic particle imaging angiography

Extraction of vascular structures, such as cerebral arteries from angiography data, is an important step in the detection and analysis of vessel anomalies and pathologies such as aneurysms. It is also important to create accurate and smooth surface models for the hemodynamic or flow dynamic analysis, functional assessment, interventional and surgical planning, and simulation. Extracted surface models can also be used for quantitative measurements and geometrical analysis of the tube-like structures in other domains. Manual segmentation is an exhaustive task for experts. Hence, various vasculature segmentation algorithms have been developed in the literature, and several reviews have been published [1, 2]. In this work, we propose a vessel tractography approach that would lend itself naturally with the ideal bright blood contrast of Magnetic Particle Imaging (MPI) [3-5]. We demonstrate this new vessel segmentation method on Magnetic Resonance Angiography (MRA) data, and extract a whole vessel tree using a high order tensor (HOT) vessel tractography approach inspired by Higher Angular Diffusion Imaging (HARDI) techniques [6,7].
Tumor-Cut: Segmentation of Brain Tumors on Contrast Enhanced MR Images for Radiosurgery Applications

In this paper, we present a fast and robust practical tool for segmentation of solid tumors with minimal user interaction to assist clinicians and researchers in radiosurgery planning and assessment of the response to the therapy. Particularly, a cellular automata (CA) based seeded tumor segmentation method on contrast enhanced T1 weighted magnetic resonance (MR) images, which standardizes the volume of interest (VOI) and seed selection, is proposed. First, we establish the connection of the CA-based segmentation to the graph-theoretic methods to show that the iterative CA framework solves the shortest path problem. In that regard, we modify the state transition function of the CA to calculate the exact shortest path solution. Furthermore, a sensitivity parameter is introduced to adapt to the heterogeneous tumor segmentation problem, and an implicit level set surface is evolved on a tumor probability map constructed from CA states to impose spatial smoothness. Sufficient information to initialize the algorithm is gathered from the user simply by a line drawn on the maximum diameter of the tumor, in line with the clinical practice. Furthermore, an algorithm based on CA is presented to differentiate necrotic and enhancing tumor tissue content, which gains importance for a detailed assessment of radiation therapy response. Validation studies on both clinical and synthetic brain tumor datasets demonstrate 80%-90% overlap performance of the proposed algorithm with an emphasis on less sensitivity to seed initialization, robustness with respect to different and heterogeneous tumor types, and its efficiency in terms of computation time.