nils-herrmann/clean_scirepeval huggingface dataset:
Understanding embryonic heart morphogenesis through automatic segmentation and confocal imaging with optical clearing

Embryonic heart morphogenesis (EHM) is a developmental process where the heart transforms from a single tube into a four-chambered pump. It is of great biological and clinical interest, but is still poorly understood for two main reasons. On one hand, the existing imaging modalities for investigating EHM suffered from either limited penetration depth or limited spatial resolution. On the other hand, current works typically adopted manual segmentation, which was tedious, subjective and time-consuming considering the complexity of developing heart geometry and the large size of images. In this paper, we propose to utilize confocal microscopy imaging with tissue optical clearing technique to image the heart at different stages of development for EHM study. This imaging method is able to produce high spatial resolution images and achieve high penetration depth. Furthermore, we propose a novel convex active contour model for automatic image segmentation. The model has the ability of dealing with intensity fall-off the depth which is characterized by confocal microscopy images. We scan and segment embryonic quail hearts from day 6 to day 14 of incubation. The experimental results show the efficiency of the proposed segmentation method and the usefulness of this imaging data, and provide us an insight view of early heart development.
A convex neighbor-constrained active contour model for image segmentation

A large number of real images possess the property of intensity non-homogeneity, which hinders them from being segmented by many image segmentation approaches. Recently, region-based active contour models utilizing local information have been introduced to segment images with intensity non-homogeneity. However, all these models are not convex, thus a good initial guess is required, which limits their practical application. In this paper, we propose a convex neighbor-constrained active contour model to segment images with intensity non-homogeneity. With different shapes and sizes of the neighborhood for each point, our model can accurately capture the region information of a given image. Our model is convex, and therefore it is independent of the initial condition and allows for automatic segmentation. To minimize energy functional of the model, we choose the efficient and fast Split Bregman method. Experimental results on synthetic and real images demonstrate the superior performance of our model.
Transmural Imaging of Ventricular Action Potentials and Post-Infarction Scars in Swine Hearts

The problem of using surface data to reconstruct transmural electrophysiological (EP) signals is intrinsically ill-posed without a unique solution in its unconstrained form. Incorporating physiological spatiotemporal priors through probabilistic integration of dynamic EP models, we have previously developed a Bayesian approach to transmural electrophysiological imaging (TEPI) using body-surface electrocardiograms. In this study, we generalize TEPI to using electrical signals collected from heart surfaces, and we test its feasibility on two pre-clinical swine models provided through the STACOM 2011 EP simulation Challenge. Since this new application of TEPI does not require whole-body imaging, there may be more immediate potential in EP laboratories where it could utilize catheter mapping data and produce transmural information for therapy guidance. Another focus of this study is to investigate the consistency among three modalities in delineating scar after myocardial infarction: TEPI, electroanatomical voltage mapping (EAVM), and magnetic resonance imaging (MRI). Our preliminary data demonstrate that, compared to the low-voltage scar area in EAVM, the 3-D electrical scar volume detected by TEPI is more consistent with anatomical scar volume delineated in MRI. Furthermore, TEPI could complement anatomical imaging by providing EP functional features related to both scar and healthy tissue.
Physiome Model Based Cardiac Information Recovery Under Multiframe State-Space Framework

In patient-specific cardiac information recovery, meaningful a priori models for constraining the sparse and noise-corrupted measurements, and also the optimal criteria for coupling them together, are essential for obtaining more reliable estimates. Although the extensively used biomechanical models give promising results, it cannot account for the active components of the myocytes. In view of this, we propose to adopt a cardiac physiome model, which is composed by a cardiac electric wave propagation model, an electro-mechanical coupling model, and a biomechanical model, for a more complete modeling of the cardiac physiology. Under the multiframe state-space filtering framework, the physiome model is coupled with the measurements to provide the optimal estimates of the cardiac information. Experiments have been performed on both synthetic and MRI data to illustrate its abilities and benefits
A meshless framework for bevel-tip flexible needle insertion through soft tissue

Flexible needle offers greater mobility and maneuverability in minimally invasive procedures. However, its nonholonomic property and complicated interactions with soft tissues makes accurate insertion and motion planning for this type of needle still a challenge. Realistically modeling and simulating its insertion through a soft tissue will provide many information to enhance the motion planning and control of the flexible needle.This paper presents a meshless framework for modeling and simulation of flexible needle insertion through soft tissue. Instead of using elements to represent the needle and soft tissue as in most existing FEM-based methods, which are connected with meshes, our approach uses two separated sets of nodes to represent them. Structure constraints as used in FEM-based methods have been released, and the needle interacts with the soft tissue using a moving least square approximation. Since no mesh is required in this approach, no remeshing or mesh modification is needed for both accuracy refinement and tissue-needle interaction. Finally, simulations results are provided to validate the performance of our approach.
Pet image reconstruction: GPU-accelerated particle filter framework

In this paper, we explore the usage of graphics processing units (GPU)-accelerated particle filter strategies for the estimation of activity map in tomographic PET imaging. The proposed framework formulates the physiological model of the imaging tissues through state space evolution equations and the photon counting statistics through observation equations, and then reconstruction is performed using particle filter estimation. For fast reconstruction, the calculations are implemented using highly-parallel GPU. Experiments show that the image quality is improved through particle filter. Furthermore, thanks to the computing power of graphics hardware, reconstruction times are practical for clinical applications.
Nonlinear cardiac deformation recovery from medical images

To recover physiologically meaningful cardiac deformation from medical images, realistic physiological models are essential to constrain the recovery process, and a statistical filtering framework is required to couple the models and images according to their respective uncertainties. As realistic cardiac models are usually nonlinear, existing cardiac deformation recovery frameworks either ignore the statistical filtering part, or linearize the model and apply linear filtering techniques such as the extended Kalman filtering. This reduces the physiological plausibility and statistical optimality of the recovery results. In this paper, we propose a nonlinear cardiac deformation recovery framework with unscented Kalman filtering which preserves the intact system nonlinearity. Experiments were done on both synthetic data and magnetic resonance images to show the benefits and clinical relevance of our framework.
Energy-minimizing deformable grids for tracking tagged MR cardiac images

Magnetic resonance imaging (MRI) is unique in being able to non-invasively and selectively alter tissue magnetization, and hence create striped grid patterns on the resulting MR images. The authors discuss preliminary results in automated tracking of motion from myocardial tags. To track the motion, energy-minimizing deformable grids are introduced. Deformable grids are energy-minimizing curves which respond to image-derived forces and relax in valleys of an appropriately defined energy landscape. Energy minimization for each grid line is carried out by using the method of dynamic programming. Tracking the valleys of the energy landscape allows for measurement of MR tag deformations due to tissue motion.<<ETX>>
Microcalcification detection based on localized texture comparison

While microcalcifications (MCs) are important early signs of breast cancers, their reliable detection from mammograms has been largely elusive for both radiologists and computer-aided diagnosis (CAD) strategies. Two of the essential components in a CAD system are the detection of the suspicious MC pixels/regions using image processing and analysis techniques, and the training, classification, and recognition of these areas based on pattern recognition methods. In this paper, we present a novel scheme to identify and classify microcalcifications based on localized texture comparison. Relying on a texture removal and repairing (R&R) process of the preselected suspicious areas from their surrounding background tissues, pre- and post- R&R local characteristic features of these areas are extracted and compared. A modified AdaBoost algorithm is then adopted to train the classifier using expert-labelled microcalcifications, followed by a clustering process. Experiments with the mammographic images from the MIAS and DDSM databases have shown very promising results.
Shape-based 4D left ventricular myocardial function analysis

Describes further efforts aimed at more accurately and objectively determining and quantifying the local, regional and global function of the left ventricle (LV) of the heart from four-dimensional (4D) image data. By studying the endocardial-epicardial surface properties of LV wall over many temporal frames within a cardiac cycle and over a range of conditions of LV wall abnormalities, one will be able to better understand the physiological processes associated with ischemic heart disease. Here, the authors first briefly describe the setup for an in vivo experimental model and the imaging techniques, including the use of implanted imaging opaque markers that will permit the authors to establish the gold standard against which the validity of their noninvasive image analysis algorithms can be evaluated, as well as permit them to study the efficacy of using in vivo, image-derived measures of function for predicting regional myocardial viability. Then, the shape-based image analysis methods, particularly the authors' new developments in surface triangulation from segmented contour stacks of LV wall, surface curvature estimation, pointwise non-rigid motion tracking, quantitative measures of LV motion and thickening, and 3D visualization techniques, are discussed. Finally, initial experimental results using image sequences from two different modalities are presented.<<ETX>>
Embedded Convolutional Face Finder

In this paper, a high-level optimization methodology is applied for the implementation of the well-known convolutional face finder (CFF) algorithm for real-time applications on cellular phone, such as teleconferencing, advanced user interfaces, pictures indexing and security access control. This face detector is based on a feature extraction and classification technique which consists in a pipeline of convolutions and subsampling operations. Design of embedded systems must find a good trade off between performance and code size due to the limited amount of resource available. We propose a methodology to cope with the main drawbacks of the CFF original implementation like floating point computation and memory allocation, to allow parallelism exploitation and perform algorithm optimizations. Results show that our embedded face detection system can accurately locate faces with less computational load and memory cost. It runs on a 275 MHz Starcore DSP at 9 QCIF images/s with state-of-the-art detection rates and very low false alarm rates
Fast and Robust Face Detection on a Parallel Optimized Architecture Implemented on FPGA

In this paper, we present a parallel architecture for fast and robust face detection implemented on FPGA hardware. We propose the first implementation that meets both real-time requirements in an embedded context and face detection robustness within complex backgrounds. The chosen face detection method is the Convolutional Face Finder (CFF) algorithm, which consists of a pipeline of convolution and subsampling operations, followed by a multilayer perceptron. We present the design methodology of our face detection processor element (PE). This methodology was followed in order to optimize our implementation in terms of memory usage and parallelization efficiency. We then built a parallel architecture composed of a PE ring and an FIFO memory, resulting in a scalable system capable of processing images of different sizes. A ring of 25 PEs running at 80 MHz is able to process 127 QVGA images per second and performing real-time face detection on VGA images (35 images per second).
A Parallel Face Detection System Implemented on FPGA

In this paper, we introduce a methodology for designing a system for face detection and its implementation on FPGA. The chosen face detection method is the well-known convolutional face finder (CFF) algorithm, which consists in a pipeline of convolutions and subsampling operations. Our goal is to define a parallel architecture able to process efficiently this algorithm. We present a dataflow based architecture algorithm adequation (AAA) methodology implemented using the SynDEx software, in order to find the best compromise between the processing power and functionality requirement of each processor element (PE), and the efficiency of algorithm parallelization. We describe a first implementation of a PE on a Virtex 4 FPGA using the DSP48 dedicated blocks. This PE is able to run at a maximum frequency of 352 MHz and occupies only 2% of a Virtex 4 SX35 device.
An Embedded Robust Facial Feature Detector

In this paper, we present a robust and optimized facial feature detector algorithm which meets the constraints of embedded processors allowing facial feature based services on mobile terminals, such as teleconferencing, advanced user interface, image indexing and security access control. The studied facial feature detector is based on convolutional neural networks, a feature extraction and classification technique which consists of a pipeline of convolution and sub-sampling operations, followed by a multi layer perceptron. The design of embedded systems requires a good trade off between performance and code size due to the limited amount of available resources. We show that such convolutional neural network can efficiently avoid any floating-point computation without any loss in performance. We also propose several algorithmic optimizations to reduce the complexity of the algorithm and avoid common drawbacks of embedded applications such as miss-caches. Experimental results show that our embedded facial feature detection system can accurately locate facial feature with less computational load. It is able to process up to 12 faces/s on an Xscale PXA27x embedded processor @ 624MHz, which represents a speed-up factor of 700 compared to the reference floating point implementation.
Combining Multi-scale Character Recognition and Linguistic Knowledge for Natural Scene Text OCR

Understanding text captured in real-world scenes is a challenging problem in the field of visual pattern recognition and continues to generate a significant interest in the OCR (Optical Character Recognition) community. This paper proposes a novel method to recognize scene texts avoiding the conventional character segmentation step. The idea is to scan the text image with multi-scale windows and apply a robust recognition model, relying on a neural classification approach, to every window in order to recognize valid characters and identify non valid ones. Recognition results are represented as a graph model in order to determine the best sequence of characters. Some linguistic knowledge is also incorporated to remove errors due to recognition confusions. The designed method is evaluated on the ICDAR 2003 database of scene text images and outperforms state-of-the-art approaches.
Watershed parallel algorithm for asynchronous processors array

A joint algorithm-architecture analysis leads to a new version of picture segmentation system adapted to multimedia mobile terminal constraints. The asynchronous processors network, with a granularity level of one processor per pixel, based on data flow model, takes less than 10 /spl mu/s to segment a SQCIF $88*72 pixels - image (about 2000 times faster than the classical sequential watershed algorithms). The main originality of the proposed algorithm is only one global synchronization point is needed in order to complete the segmentation transformation, instead of the three (or more) classical points: minima detection, labelization and flooding. Our system tends to cope with multimedia mobile phones constraints, i.e. real time computing circuit, low power. We have simulated and validated this system thanks to "SystemC" library; VHDL synchronous prototyping shows up results accordingly.
ICDAR2015 competition on Text Image Super-Resolution

This paper presents the first international competition on Text Image Super-Resolution (SR) and the ICDAR2015-TextSR dataset. We describe the core of the competition: interest, dataset generation and evaluation procedure, together with participating teams and their respective methods. The obtained results, along with baseline image upscaling schemes and state-of-the-art SR approaches are reported and commented. The main conclusion of this competition is that SR systems may improve OCR performances by up to 16.55 points in accuracy compared with bicubic interpolation for the proposed low resolution images.
Embedded facial image processing with Convolutional Neural Networks

This paper presents an embedded facial image analysis framework based on Convolutional Neural Networks (ConvNets). This robust framework has been proposed by Garcia, Delakis and Duffner on general purpose workstations without any constraints on computational and memory resources. We show that ConvNets, which consist of a pipeline of convolution and subsampling operations followed by a Multi Layer Perceptron, are particularly well suited for implementation on embedded processors. We present a set of high-level optimizations, such as automatic fractional transformation, convolution and subsam-pling fusion and memory requirement optimizations that can be applied to these algorithms without any loss in performance, leading to a speedup factor up to 700 compared to the reference implementation. This work leads to a face processing library able to handle the complete framework and its applications on mobile phones.
Speckle Patch Similarity for Echogenicity-Based Multiorgan Segmentation in Ultrasound Images of the Thyroid Gland

Ultrasound (US) imaging deals with forming a brightness image from the amplified backscatter echo when an ultrasound wave is triggered at the region of interest. Imaging artifacts and speckles occur in the image as a consequence of backscattering and subsequent amplification. We demonstrate the usefulness of speckle-related pixels and imaging artifacts as sources of information to perform multiorgan segmentation in US images of the thyroid gland. The speckle-related pixels are clustered based on a similarity constraint to quantize the image. The quantization results are used to locate useful anatomical landmarks that aid the detection of multiple organs in the image, which are the thyroid gland, the carotid artery, the muscles, and the trachea. The spatial locations of the carotid artery and the trachea are used to estimate the boundaries of the thyroid gland in transverse US scans. Experiments performed on a multivendor dataset yield good quality segmentation results with probabilistic Rand index  $ > $ 0.83 and boundary error  $ < $ 1 mm, and an average accuracy greater than 94%. Analysis of the results using the Dice coefficient as the metric shows that the proposed method performs better than the state-of-the-art methods. Further, experiments conducted on 971 images of a publicly available dataset prove the effectiveness of the algorithm to track the carotid artery for guided interventions. In addition to US-guided interventions, the algorithm can be used as a general framework in applications pertaining to volumetric analysis and computer-aided diagnosis.
Fetal heart rate detection using VPW-FRI

Fetal Electrocardiograms (fECG) are an inexpensive and noninvasive method to determine the heart rate (HR) of the fetus. Large variations in the fetal HR is a good indicator that the fetus is in distress thus allowing for clinical intervention. Although advances have been made in the field of fetal HR detection, more can be done to improve accuracy and efficiency. The Variable Pulse Width - Finite Rate of Innovation (VPW-FRI) method is a suitable method given it deals with pulse parameters such as location, width and amplitude. This allows it to automatically segment and identify the foetal QRS complexes and R peak locations from compressed samples which in turn would yield the fetal HR. Our method, which includes model based denoising and multichannel capability, is comparable to other methods involving machine learning, wavelets and ICA.
Compression of neonatal EEG seizure signalswith finite rate of innovation

Analyses of neonatal EEG seizures and subsequent diagnoses can only be done effectively on long-term recordings on the condition that the morphology of the EEG signals are retained. Therefore, a reliable, accurate and efficient compression and reconstruction technique is necessary to store and retrieve the data. In this paper, we propose a new compression technique for neonatal EEG seizure signals via sampling theory developed for signals with a finite rate of innovation. Firstly, the EEG seizure signals are modeled as periodic nonuniform linear splines. Next, through the sampling and reconstruction scheme developed for signals with finite rate of innovation, we show that neonatal EEG seizure signals can be highly compressed while preserving their morphologies.
A no-reference perceptual blur metric

We present a no-reference blur metric for images and video. The blur metric is based on the analysis of the spread of the edges in an image. Its perceptual significance is validated through subjective experiments. The novel metric is near real-time, has low computational complexity and is shown to perform well over a range of image content. Potential applications include optimization of source coding, network resource management and autofocus of an image capturing device.
Automatic Anterior Chamber Angle Assessment for HD-OCT Images

Angle-closure glaucoma is a major blinding eye disease and could be detected by measuring the anterior chamber angle in the human eyes. High-definition OCT (Cirrus HD-OCT) is an emerging noninvasive, high-speed, and high-resolution imaging modality for the anterior segment of the eye. Here, we propose a novel algorithm which automatically detects a new landmark, Schwalbe's line, and measures the anterior chamber angle in the HD-OCT images. The distortion caused by refraction is corrected by dewarping the HD-OCT images, and three biometric measurements are defined to quantitatively assess the anterior chamber angle. The proposed algorithm was tested on 40 HD-OCT images of the eye and provided accurate measurements in about 1 second.