nils-herrmann/clean_scirepeval huggingface dataset:
A novel CAD system for autism diagnosis using structural and functional MRI

This paper introduces a comprehensive computer-aided diagnosis (CAD) system for autism diagnosis that integrates anatomical and functional information of the brain using both structural and functional magnetic resonance (MR) brain images. In order to move towards the idea of personalized medicine, analysis of the brain's Brodmann areas (BAs) is conducted to reach a diagnosis decision on the local areas. This local analysis will help clinicians allocate the subject on the autism spectrum as well as correlate autism abnormalities to the areas responsible for certain skills, such as cognitive ones. The diagnosis is done through several analysis steps on both structural and functional MRI volumes. First, anatomical features are extracted from the cerebral cortex (Cx) and the cerebral white matter (CWM) of structural MR images. A monetary reward functional MR experiment is also held to get the areas of activation in the brains of the participants in response to the applied task. Next, all the extracted features are fed to a multi-level deep network for both local and global diagnosis. The CAD system has been evaluated using subjects from the NDAR database (23 – 210 months), achieving a global classification accuracy of 94.7% based on fusing both modalities. Moreover, brain maps are shown for different autistic subjects to indicate the strength of association of each BA with autism, which supports the idea of personalized medicine. The proposed CAD system, along with the idea of local feature extraction and diagnosis, holds the promise for being capable to resolve autism endophenotypes.
A novel framework for automatic segmentation of kidney from DW-MRI

The segmentation of the kidney tissues is a key step in developing any non-invasive computer-aided diagnostic (CAD) system for early detection of acute renal transplant rejection. This paper introduces a geometric (level-set)-based deformable model approach for the 3D kidney segmentation from diffusion-weighted magnetic resonance imaging (DW-MRI). The proposed deformable model is guided by a stochastic speed relationship based on an adaptive shape prior guided by the visual appearance of the DW-MRI data. The voxel-wise guiding of the level-sets is obtained by integrating these three image features into a joint Markov-Gibbs random field (MGRF) model of the kidney and its background. The segmentation approach was evaluated for 40 DW-MRI data sets acquired at b-values ranging from 0 to 1000 s/mm2 and compared against other segmentation methods using three evaluation metrics: the Dice similarity coefficient (DSC), the 95-percentile modified Hausdorff distance, and the absolute kidney volume difference. Experimental results' evaluation between manually drawn and automatically segmented contours confirm the robustness and accuracy of the proposed approach.
A novel Gaussian Scale Space-based joint MGRF framework for precise lung segmentation

A new framework for the precise segmentation of lung tissues from Computed Tomography (CT) is proposed. The CT images, Gaussian Scale Space (GSS) data generation using Gaussian Kernels (GKs), and desired maps of regions (lung and the other chest tissues) are described by a joint Markov-Gibbs Random Field Model (MGRF) of independent image signals and interdependent region labels. We focus on the most accurate model identification of the joint MGRF models. To better specify region borders, each empirical distribution of signals is rigorously approximated by a Linear Combination of Discrete Gaussians (LCDG) with positive and negative components. The classical Expectation-Maximization (EM) algorithm has been adapted for the LCDG model. The initial segmentations from the original and the generated GSS CT images are based on the LCDG-models; then they are iteratively refined using an MGRF model with analytically estimated potentials. Finally, these initial segmentations are fused together using a Bayesian fusion approach to get the final segmentation of the lung region. Experiments on eleven real data sets based on Dice Similarity Coefficient (DSC) metric confirms the high accuracy of the proposed approach.
Improving full-cardiac cycle strain estimation from tagged CMR by accurate modeling of 3D image appearance characteristics

To reduce noise within a tag line, unsharpen tag edges in the spatial domain, and amplify the tag-to-background contrast, a 3D energy minimization framework for the enhancement of tagged Cardiac Magnetic Resonance (CMR) images, that is based on first- and second-order learned visual appearance models, is proposed. The first-order appearance modeling uses an adaptive Linear Combination of Discrete Gaussians (LCDG) to accurately approximate the empirical marginal probability distribution of CMR signals for a given sequence, and to separate the tag and background submodels. It is also used to classify the tag lines and the background. The second-order model considers image sequences as samples of a translation- and rotation-invariant 3D Markov-Gibbs Random Field (MGRF), with multiple pairwise voxel interactions. A 3D energy function for this model is built by using the analytical estimation of the spatiotemporal geometry and the Gibbs potentials of interaction. To improve the strain estimation, through enhancement of the tag and background homogeneity and contrast, the given sequence is adjusted using comparisons to the energy minimizer. Special 3D geometric phantoms, motivated by the statistical analysis of the tagged CMR data, have been designed to validate the accuracy of our approach. Experiments with the phantoms and eight in-vivo data sets have confirmed the high accuracy of functional parameter estimation for the enhanced CMR images when using popular spectral techniques, such as spectral Harmonic Phase (HARP).
A novel framework for the segmentationof mrinfant brain images

This paper introduces a novel adaptive atlas-based framework for the automated segmentation of different brain structures from infant magnetic resonance (MR) brain images. The proposed framework provides a more accurate segmentation of different infant brain structures in the isointense age stage (6-12 months) by integrating diffusion tensor imaging (DTI) image features (e.g., fractional anisotropy (FA)) in the segmentation procedure. The input to the proposed system is the medical scans of the infant brain, i.e., 4D diffusion weighted images (DWI). The input brain first undergoes a quality control procedure to remove scan artifacts, and correct motion and eddy current distortions, followed by brain extraction, in which any non-brain tissues are removed. Then, specific DTI features are extracted, and fused to guide the segmentation process to produce the final segmented brain tissues. The high accuracy of the proposed segmentation approach was confirmed by testing it on 10 in-vivo diffusion weighted infant MR brain data sets using three metrics: the Dice coefficient, the 95-percentile modified Hausdorff distance, and the absolute volume difference.
Appearance-based diagnostic system for early assessment of malignant lung nodules

A novel 2D approach for early assessment of malignant lung nodules based on analyzing the spatial distribution of Hounsfield values for the detected lung nodules is proposed. Spatial distribution of Hounsfield values comprising the malignant nodule appearance is accurately modeled with a new 2D rotationally invariant second-order Markov-Gibbs Random Field (MGRF). Preliminary experiments on 109 lung nodules (51 malignant and 58 benign) show that the proposed method is a promising supplement to current technologies (biopsy-based diagnostic systems) for the early diagnosis of lung cancer.
EM Based Approximation of Empirical Distributions with Linear Combinations of Discrete Gaussians

We propose novel expectation maximization (EM) based algorithms for accurate approximation of an empirical probability distribution of discrete scalar data. The algorithms refine our previous ones in that they approximate the empirical distribution with a linear combination of discrete Gaussians (LCDG). The use of the DGs results in closer approximation and considerably better convergence to a local likelihood maximum compared to previously involved conventional continuous Gaussian densities. Experiments in segmenting multimodal medical images show the proposed algorithms produce more adequate region borders.
A new CAD system for early diagnosis of dyslexic brains

The importance of accurate early diagnosis of dyslexia, which severely affects the learning abilities of children, cannot be overstated. Neuropathological studies have revealed an abnormal anatomy of the cerebral white matter (CWM) in dyslexic brains. We explore a possibility of distinguishing between dyslexic and normal (control) brains by a quantitative shape analysis of CWM gyrifications on 3D magnetic resonance (MR) images. Our approach consists of (i) segmentation of the CWM on a 3D brain image using a deformable 3D boundary; (ii) extraction of gyrifications from the segmented CWM, and (iii) shape analysis to quantify thickness of the extracted gyrifications and classify dyslexic and normal subjects. The boundary evolution is controlled by two probabilistic models of visual appearance of 3D CWM: the learned prior and the current appearance model. Initial experimental results suggest that the proposed 3D texture analysis is a promising supplement to the current techniques for diagnosing dyslexia.
Automatic 3-D muscle and fat segmentation of thigh magnetic resonance images in individuals with spinal cord injury

Spinal cord injured (SCI) individuals are often subject to skeletal muscles deterioration and adipose tissue gain in paralyzed muscles. These negative impacts can limit motor functions and lead to secondary complications such as diabetes, cardiovascular diseases and metabolic syndrome. In this study, we proposed an accurate and fast automatic framework for thigh muscle and fat volume segmentation using magnetic resonance 3-D images, which is aimed at quantifying the impact of SCI and different rehabilitative interventions for these individuals. In this framework, the subcutaneous, intermuscular fat volumes were segmented using a Linear Combination of Discrete Gaussians (LCDG) algorithm. In order to segment muscle group volumes, each MRI volume was initially registered to a training database using a 3-D Cubic B-splines based method. As a second step, a 3-D level-set method was developed utilizing the Joint Markov Gibbs Random Field (MGRF) model that integrates first order appearance model of the muscles, spatial information, and shape model to localize the muscle groups. The results of testing the new method on 15 MRI datasets from 10 SCI and 5 non-disabled subjects showed accuracy of 87.10% for fat segmentation and 96.71% for muscle group segmentation based on Dice similarity coefficient measurements.
An integrated framework for automatic clinical assessment of diabetic retinopathy grade using spectral domain OCT images

Diabetic retinopathy (DR) is a progressive disease and its detection at an early stage is crucial for saving a patient's vision. In this paper, an enhanced computer-assisting diagnostic (CAD) system is developed for the discovery and grading of non-proliferative DR from optical coherence tomography (OCT) images. The proposed CAD system elaborates three sequential stages. Initially, 12 distinct retina layers are localized using our previously developed segmentation approach based on an integrated joint model that combines shape, intensity, and spatial information. Secondly, three features, namely the reflectivity, curvature, and thickness are quantitatively measured from the segmented layers. Finally, a two-stage deep fusion classification network (DFCN), trained by stacked non-negativity constraint autoencoder (SNCAE), is used first to classify the subject as normal or DR, then assess the grade of DR as either early stage or mild/moderate. Using "leave-one-subject-out" experiments on a dataset of 74 OCT images, the CAD system distinguished between normal and DR subjects with a 93% accuracy (sensitivity =91%, specificity =97%) and achieved a 98% correct classification between early stage and mild/moderate DR. These results confirm the proposed framework as a reliable non-invasive diagnostic tool.
A Novel Approach for Automatic Follow-Up of Detected Lung Nodules

Our long term research goal is to develop an image-based approach for early diagnosis of lung nodules that may lead to lung cancer. This paper focuses on monitoring the progress of detected lung nodules in successive chest low dose CT (LDCT) scans of a patient using non-rigid registration. In this paper, we propose a new methodology for 3D LDCT data registration.  using the learned prior appearance model followed by local alignments in order to correct for intricate deformations. From two subsequent chest scans, visual appearance of the chest images, after equalizing their signals, are modeled with a Markov-Gibbs random field with pairwise interaction. Our approach is based on finding the affine transformation to register one data set (target data) to another data set (reference data) by maximizing a special Gibbs energy function using a gradient descent algorithm. To get accurate appearance model, we developed a new approach to an automatically select the most important cliques that describe the visual appearance of LDCT data. To handle local deformations, we propose a new approach based on deforming each voxel over evolving closed and equi-spaced surfaces (iso-surfaces) to closely match the prototype. The evolution of the iso-surfaces is guided by an exponential speed function in the directions minimizing distances between corresponding pixel pairs on the iso-surfaces on both data sets. Our preliminary results on 10 patients show that the proper registration could lead to precise identification of the progress of the detected lung nodules.
Using physiological signal analysis to design affective VR games

One of the most important factors in human-computer interaction is the user emotional reaction. Interactive environments including serious games that are responsive to user emotions improve their effectiveness and user acceptances. Testing and training for user emotional competence is meaningful in health care, which has motivated us to analyze immersive affective games using feedback. In this article, a systematic model of designing interactive environment is presented, which consists three modules as affect modeling, affect recognition, and affect control. For affect control module, a graph-based structure is described to control the game scenarios dynamically by analyzing user emotional states. The affect recognition module makes use of physiological signal analysis such as autonomic nervous system variables to evaluate user emotional reaction to different stimuli, then send it as biofeedback to affect control module. The analysis should be used as a guiding principle for designing affective serious games. Oculus Rift DK2 is used in experiments to provide immersive virtual reality with affective scenarios. The experiments demonstrate the ability to induce measurable and distinguishable emotions from physiological signals. The measurement of effectiveness is discussed. Possible applications include emotion competence training for children with autism spectrum disorder.
Image Analysis of Renal DCE MRI for the Detection of Acute Renal Rejection

Acute rejection is the most common reason of graft failure after kidney transplantation, and early detection is crucial to survive the transplanted kidney function. In this paper we introduce a new approach for the automatic classification of normal and acute rejection transplants from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). The proposed algorithm consists of three main steps; the first step isolates the kidney from the surrounding anatomical structures. In the second step, a novel nonrigid-registration algorithm is employed to account for the motion of the kidney due to patient breathing, and finally, the perfusion curves that show the transportation of the contrast agent into the tissue are obtained from the cortex and used in the classification of normal and acute rejection transplants. Applications of the proposed approach yield promising results
Unsupervised Segmentation of Multi-Modal Images by a Precise Approximation of Individual Modes with Linear Combinations of Discrete Gaussians

Many important applications of image analysis deal with multi-modal images such that each object of interest relates to an individual mode in the marginal signal distribution collected over the image. Segmentation of such seemingly simple images is nonetheless a challenging problem because each meaningful boundary between the objects is rarely formed by easily detectable signal differences (or "edges"). Most commonly, the signals have very close values across the boundary and relate to intersecting tails of distributions describing individual objects. To accurately segment such images, not only the main body but also the tails of each such distribution have to be precisely recovered from the available mixture. We present a re.ned version of our novel EM-based algorithm for accurate unsupervised segmentation of multi-modal grayscale images. It has a considerably improved convergence to a local maximum of the image likelihood and provides a very close approximation of each distribution related to the mode with a linear combination of sign-alternate discrete Gaussian kernels. Experiments with medical images show the proposed segmentation is more accurate than several other known alternatives.
Level Set Segmentation Using Statistical Shape Priors

A novel and robust 3-D segmentation approach is proposed using level sets based on shape constraints. The approach depends on both the gray level and shape information. A partial differential equation (PDE) is developed to control the evolution of the level sets. The PDE does not include weighting coefficients to be tuned, overcoming the disadvantages of other PDE approaches. The shape information is gathered from a set of the signed distance maps representing the training data as a histogram of the occurrences of the points inside and outside the object. We use a novel statistical approach to get a probability density function (pdf) for the signed distance map of the points inside and outside and also the distribution of gray level inside and outside the object. The proposed statistical approach is based on modelling the empirical density function (normalized histogram of occurrence) for either the gray level distribution or signed distance map with a linear combination of Gaussians (LCG) with positive and negative components. We modify an Expectation-Maximization (EM) algorithm to deal with the LCGs and also propose a novel EM-based sequential technique to get a close initial LCG approximation for the modified EM algorithm to start with. The pdf’s of the signed distance and intensity gray level are embedded in the speed function of the level set specifying the direction of evolution. Experimental results show how the approach is accurate in segmenting different types of 2-D and 3-D data sets including medical images.
Precise Segmentation of 3-D Magnetic Resonance Angiography

Accurate automatic extraction of a 3-D cerebrovascular system from images obtained by time-of-flight (TOF) or phase contrast (PC) magnetic resonance angiography (MRA) is a challenging segmentation problem due to the small size objects of interest (blood vessels) in each 2-D MRA slice and complex surrounding anatomical structures (e.g., fat, bones, or gray and white brain matter). We show that due to the multimodal nature of MRA data, blood vessels can be accurately separated from the background in each slice using a voxel-wise classification based on precisely identified probability models of voxel intensities. To identify the models, an empirical marginal probability distribution of intensities is closely approximated with a linear combination of discrete Gaussians (LCDG) with alternate signs, using our previous EM-based techniques for precise linear combination of Gaussian-approximation adapted to deal with the LCDGs. The high accuracy of the proposed approach is experimentally validated on 85 real MRA datasets (50 TOF and 35 PC) as well as on synthetic MRA data for special 3-D geometrical phantoms of known shapes.
Segmentationof pathological lungs from CT chest images

A novel framework for precise segmentation of pathological lung tissues from computed tomography (CT) is presented. The proposed segmentation method is based on a novel 3D joint Markov-Gibbs random field (MGRF) model that integrates three features: (i) the first-order visual appearance model of the CT image, (ii) the second-order spatial interaction model of the CT image, and (iii) a shape prior model of the lung. The first-order appearance model describes the empirical distribution of image signals using a linear combination of Discrete Gaussians (LCDG) with positive and negative components. The second order spatial interaction model describes the relation between the CT image signals using a pairwise MGRF spatial model of independent image signals and interdependent region labels. The shape prior is constructed from a set of training CT data, collected from different subjects. Experiments on 20 datasets with different types of pathologies confirm high accuracy of the proposed approach compared with other lung segmentation methods.
Assessment of exercise-induced immune cell apoptosis using morphological image processing

This paper presents an image processing-based approach for the assessment of living cell apoptosis. The approach uses deformable models in tracing the changes in cell shapes in accordance to possible apoptotic cell death. Stochastic controlled energy function is used to drive the evolution of the deformable model. As an application for the proposed approach, it is applied for detecting the morphological changes in exercise-induced immune cells during their apoptosis. The approach is used with the goal of automating the assessment of cell apoptosis. The proposed approach shows promising results in detecting these morphological changes.
A new 3D automatic segmentation framework for accurate segmentation of prostate from DCE-MRI

Prostate segmentation is an essential step in developing any noninvasive Computer-Assisted Diagnostic (CAD) system for the early diagnosis of prostate cancer using Magnetic Resonance Images (MRI). In this paper, a novel framework for 3D segmentation of the prostate region from Dynamic Contrast Enhancement MRI (DCE-MRI) is proposed. The framework is based on Maximum A Posteriori (MAP) estimate of a new log-likelihood function that consists of : (i) 1st-order visual appearance descriptors of the DCE-MRI, (ii) a 3D spatially rotation-variant 2nd-order homogeneity descriptor, and (iii) a 3D prostate shape descriptor. The shape prior is learned from the co-aligned 3D segmented prostate data. The visual appearances of the object and its background are described with marginal gray-level distributions obtained by separating their mixture over prostate data. The spatial interactions between the prostate voxels are modeled by a 3D 2nd-order rotation-variant Markov-Gibbs Random Field (MGRF) of object/background labels with analytically estimated potentials. Experiments with in vivo prostate DCE-MRI confirm the robustness and accuracy of the proposed approach.