nils-herrmann/clean_scirepeval huggingface dataset:
A statistical framework for the classification of infant DT images

This paper introduces a new adaptive atlas-based framework for the automated segmentation of different brain structures from infant diffusion tensor images (DTI). To model the brain images and their desired region maps, we used a joint Markov-Gibbs random field (MGRF) model that accounts for three image descriptors: (i) a 1st-order visual appearance to describe the empirical distribution of DTI extracted features, (ii) an adaptive shape model, and (iii) a 3D spatially invariant 2nd-order MGRF homogeneity descriptor. The 1st-order visual appearance descriptor is accurately modeled using a linear combination of discrete Gaussians (LCDG) model having positive and negative components. The proposed adaptive shape model is constructed from a prior atlas database built using a subset of co-aligned training data sets that is adapted during the segmentation process guided by the visual appearance characteristics of several DTI features. To accurately account for the large inhomogeneity of infant brains, the homogeneity descriptor is modeled by a 2nd-order translation and rotation invariant MGRF of region labels with analytically estimated potentials. The high accuracy of our segmentation approach was confirmed by testing it on 10 in-vivo infant DTI brain data sets using three metrics: the Dice similarity coefficient, the 95-percentile modified Hausdorff distance, and the absolute brain volume difference.
3D automatic approach for precise segmentation of the prostate from Diffusion-Weighted Magnetic Resonance Imaging

Prostate segmentation is an essential step in developing any non-invasive 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 Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is proposed. The framework is based on a Maximum A Posteriori (MAP) estimate of a new log-likelihood function that accounts for Markov-Gibbs shape and appearance models of the object-of-interest and its background. The framework was evaluated on in vivo prostate DW-MRI with available manual expert segmentation. The performance evaluation of the proposed segmentation approach, based on voxel-based and distance-based metrics between manually drawn and automatically segmented contours, confirmed the robustness and accuracy of the proposed segmentation approach.
Pararmeter estimation in gibbs-markov image models

This paper introduces two novel approaches to estimate the clique potentials in binary and multilevel realizations of Gibbs Markov random field (GMRF) models. The first approach employs a genetic algorithm (CA) in order to arrive at the closest synthesized image that resembles the original "observed" image. The Second approach is used to estimate the parameters of Gaussian Markov random field. Given an image formed of a number of classes, an initial class density is assumed and the parameters of the densities are estimated using the EM approach. Convergence to the true distribution is tested using the Levy distance. The segmentation of classes is performed iteratively using the ICM algorithm and a genetic algorithm (GA) search approach that provides the maximum a posteriori probability of pixel classification. During the iterations, the GA approach is used to select tbe clique potentials of the Gibbs-Markov models used for the observed image. The algorithm stops when a fitness function, equivalent to the maximum a posteriori probability, does not change. The approach bas been tested on synthetic and real images and is shown to
Deformable model guided by stochastic speed with application in cine images segmentation

A new speed function to guide evolution of a level-set based active contour is proposed for segmenting an object from its background in a given image. The guidance accounts for a learned spatially variant statistical shape prior, 1st-order visual appearance descriptors of the contour interior and exterior (associated with the object and background, respectively), and a spatially invariant 2nd-order homogeneity descriptor. The shape prior is learned from a subset of co-aligned training images. The visual appearances are described with marginal gray level distributions obtained by separating their mixture over the image. The evolving contour interior is modeled by a 2nd-order translation and rotation invariant Markov-Gibbs random field of object / background labels with analytically estimated potentials. Experiments to segment the inner cavity of heart cine images confirm robustness and accuracy of the proposed approach.
Image-based CAD system for accurate identification of lung injury

This paper proposes a novel framework for the identification of the radiation-induced lung injury (RILI) after radiation therapy (RT) using 4D computed tomography (CT) scans. The proposed methodology consists of four components: (i) elastic image registration; (ii) segmentation of the lung fields; (iii) extraction of functional and texture features; and (iv) classification of the lung tissues. The registration step locally aligns the consecutive phases of the respiratory cycle using an elastic image registration approach based on descent minimization of the sum of squared difference similarity metric. Secondly, lung fields are segmented using a hybrid framework that integrates an adaptive shape prior model, a first-order intensity model, and a second order homogeneity descriptor of the lung tissues. Next, regional features that describe both the texture features using the novel 7th-order Markov-Gibbs random field (MGRF) model in addition to the lung functionality features (e.g., ventilation and elasticity) are estimated from a segmented lungs. Finally, a random forest classifier (RF) is applied to distinguish between injured and normal lung tissues. To evaluate the proposed framework, we used data sets that have been collected from 13 patients who had underwent RT treatment. Experimental results demonstrate the promise of the proposed framework for the identification of the injured lung region, and thus hold the promise as a valuable tool for early detection of RILI.
A comprehensive framework for early assessment of lung injury

A novel framework for the detection of radiation-induced lung injury (RILI) from 4D computed tomography (CT) has been proposed. Our framework performs 4D-CT lung fields segmentation, deformable image registration (DIR), extraction of textural and functional features, and classification of lung voxels using deep 3D convolutional neural networks (CNN). The 4D-CT images segmentation extracts the lung fields inside the exhale phase using our multi-scale Gaussian adaptive shape prior technique followed by label propagation to other 4D-CT phases using a newly developed adaptive shape model. Then, the 4D-CT DIR locally aligns consecutive phases of the respiratory cycle using the 3D Laplace equation for finding voxel correspondences between the iso-surfaces for the fixed and moving lungs and generalized Gaussian Markov random field (GGMRF) as an anatomical consistency constraint. In addition to common lung functionality features, such as ventilation and elasticity, specific regional textural features are estimated by modeling the segmented images as samples of a novel 7th-order contrast-offset-invariant Markov-Gibbs random field (MGRF). Finally, a deep 3D CNN is applied to distinguish between the injured and normal lung tissues. 4D-CT datasets collected from 13 patients, who undergone the radiation therapy (RT), have been used in the evaluation of the proposed framework. The experimental results show the promise of our framework.
A new framework for automated identification of pathological tissues in contrast enhanced cardiac magnetic resonance images

A novel automated framework for quantification of myocardial viability in contrast enhanced cardiac magnetic resonance images (CE-CMRI) is proposed. The framework consists of three main steps. First, the inner and outer borders of the left ventricle (LV) wall (myocardium wall) are segmented from the surrounding tissue. Second, the pathological tissue in the myocardium wall is identified using a MAP-based classifier based on the visual appearance and spatial interaction of the LV pathological tissue as well as healthy tissue. Third, the myocardial viability is assessed and quantified based on measuring two parameters: the percentage of pathological tissue with respect to the area of the myocardium wall and the transmural extent of the pathological tissue in the myocardium wall. The transmural extent is estimated based on a new Partial Differential Equation (PDE) approach to determine point-to-point correspondences between the inner and outer borders of the pathological area as well as the myocardium wall. The proposed framework was tested on in-vivo CE-CMR images and validated with manual expert delineations of pathological tissue. Experiments and comparison results on real CE-CMR images confirm the robustness and accuracy of the proposed framework over the existing ones.
Atlas-based approach for the segmentation of infant DTI MR brain images

In this paper, we propose a new adaptive atlas-based technique for the automated segmentation of brain tissues (white matter and grey matter) from infant diffusion tensor images (DTI). Brain images and desired region maps (brain, Cerebrospinal fluid, etc.) are modeled by a joint Markov-Gibbs random field (MGRF) model of independent image signals and interdependent region labels. The proposed joint MGRF model accounts for the following three descriptors: (i) a 1st-order visual appearance to describe the empirical distribution of six features that has been estimated from the DTI in addition to the non-diffusion (b0) scans, (ii) 3D probabilistic atlases, and (iii) a 3D spatially invariant 2nd-order homogeneity descriptor. The 1st-order visual appearance descriptor, assuming each of the estimated DTI parameters are independent, is precisely approximated using our previously developed linear combination of discrete Gaussians (LCDG) intensity model that includes positive and negative Gaussian components. The 3D probabilistic atlases are learned using a subset of the 3D co-aligned training DTI brain images. The 2nd-order homogeneity descriptor is modeled by a 2nd-order translation and rotation invariant MGRF of region labels, with analytically estimated potentials. We tested our approach on 25 DTI brain images, and evaluated the performance on 5 manually segmented 3D DTI brain images to confirm the high accuracy of the proposed approach, as evidenced by the Dice similarity, Hausdorff distance, and absolute volume difference metrics.
A new validation approach for the growth rate measurement using elastic phantoms generated by state-of-the-art microfluidics technology

Our long-term research goal is to develop a fully automated, image-based diagnostic system for early diagnosis of pulmonary nodules that may lead to lung cancer. This paper focuses on validating our approach for monitoring the development of lung nodules detected in successive chest low dose computed tomography (LDCT) scans of a patient. Our methodology for monitoring the detected lung nodules includes 3-D LDCT data registration, which is non-rigid and involves two steps: (i) global target-to-prototype alignment of one scan to another using the learned prior appearance model followed by (ii) local alignment in order to correct for intricate relative deformations. This approach has been validated on elastic lung phantoms constructed using state-of-the-art microfluidics technology. The elastic lung phantoms are fabricated from a flexible transparent polymer, i.e., polydimethylsiloxane (PDMS). These Phantoms mimic the contractions and expansions of the lung and nodules seen during normal breathing. Experiments confirm the high accuracy of the proposed approach for measuring the growth rate of the detected lung nodules.
Computer-Aided Diagnostic System for Early Detection of Acute Renal Transplant Rejection Using Diffusion-Weighted MRI

<italic>Objective:</italic> Early diagnosis of acute renal transplant rejection (ARTR) is critical for accurate treatment. Although the current gold standard, diagnostic technique is renal biopsy, it is not preferred due to its invasiveness, long recovery time (1–2 weeks), and potential for complications, e.g., bleeding and/or infection. <italic>Methods:</italic> This paper presents a computer-aided diagnostic (CAD) system for early ARTR detection using (3D + <inline-formula><tex-math notation="LaTeX">$b$</tex-math></inline-formula>-value) diffusion-weighted (DW) magnetic resonance imaging (MRI) data. The CAD process starts from kidney tissue segmentation with an evolving geometric (level-set-based) deformable model. The evolution is guided by a voxel-wise stochastic speed function, which follows from a joint kidney-background Markov–Gibbs random field model accounting for an adaptive kidney shape prior and on-going kidney-background visual appearances. A B-spline-based three-dimensional data alignment is employed to handle local deviations due to breathing and heart beating. Then, empirical cumulative distribution functions of apparent diffusion coefficients of the segmented DW-MRI at different <inline-formula><tex-math notation="LaTeX">$b$</tex-math></inline-formula>-values are collected as discriminatory transplant status features. Finally, a deep-learning-based classifier with stacked nonnegative constrained autoencoders is employed to distinguish between rejected and nonrejected renal transplants. <italic>Results:</italic> In our initial “leave-one-subject-out” experiment on 100 subjects, <inline-formula><tex-math notation="LaTeX">$\text{97.0}\%$</tex-math></inline-formula> of the subjects were correctly classified. The subsequent four-fold and ten-fold cross-validations gave the average accuracy of <inline-formula><tex-math notation="LaTeX">$\text{96.0}\%$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\text{94.0}\%$</tex-math></inline-formula>, respectively. <italic>Conclusion:</italic> These results demonstrate the promise of this new CAD system to reliably diagnose renal transplant rejection. <italic>Significance:</italic> The technology presented here can significantly impact the quality of care of renal transplant patients since it has the potential to replace the gold standard in kidney diagnosis, biopsy.
A NEW IMAGE ANALYSIS APPROACH FOR AUTOMATIC CLASSIFICATION OF AUTISTIC BRAINS

Autism is a developmental disorder characterized by social deficits, impaired communication, and restricted and repetitive patterns of behavior. Recent neuropathological studies of autism have revealed abnormal anatomy of the cerebral white matter (CWM) in autistic brains. In this paper we introduced a novel approach to classify autistic from normal subjects based on a new shape analysis of cerebral white matter gyrifications for both normal and autistic subjects. The proposed shape analysis technique consists of three main steps. The first step is to segment cerebral white matter from proton density MRI images using a priorly learned visual appearance model for the 3D cerebral white matter in order to control the evolution of deformable boundaries. The appearance prior is modeled with a translation and rotation invariant Markov-Gibbs random field of voxel intensities with a pairwise interaction model. The second step is to extract the gyrifications of cerebral white matter from the segmented cerebral white matter. The last step is to perform shape analysis to quantify the thickness of the extracted cerebral white matter gyrifications for both autistic and normal subjects. The preliminary results of the proposed image analysis has yielded promising results that would, in the near future, supplement the use of current technologies for diagnosing autism
Segmentation of infant brain MR images based on adaptive shape prior and higher-order MGRF

This paper introduces a new framework for the segmentation of different brain structures from 3D infant MR brain images. The proposed segmentation framework is based on a shape prior built using a subset of co-aligned training images that is adapted during the segmentation process based on higher-order visual appearance characteristics of infant MRIs. These characteristics are described using voxel-wise image intensities and their spatial interaction features. In order to more accurately model the empirical grey level distribution of infant brain signals, a Linear Combination of Discrete Gaussians (LCDG) is used that has positive and negative components. Also to accurately account for the large inhomogeneity in infant MRIs, a higher-order Markov Gibbs Random Field (MGRF) spatial interaction model that integrates third- and fourth-order families with a traditional second-order model is proposed. The proposed approach was tested on 40 in-vivo infant 3D MR brain scans, having their ground truth created by an expert radiologist, using three metrics: the Dice coefficient, the 95-percentile modified Hausdorff distance, and the absolute brain volume difference. Experimental results promise an accurate segmentation of infant MR brain images compared to current open source segmentation tools.
Shape-constraint for accurate segmentation in remote sensing imagery

A new approach is proposed for the segmentation of remote sensing images which is based on using a prior shape information. This information is obtained from a set of signed distance functions (SDF). Each SDF represents the training data set as a histogram of the occurrences of the data points inside and outside a specific object. A statistical approach is proposed for modeling each SDF, which is considered to have a random distribution. The mean field theory-based support vector machines (MF-based SVM) density estimator is used to estimate the probability density function which underlies the random distribution of the SDF. The statistical modeling of shapes enables realistic representation of objects especially those which are not contiguous within the image; i.e., objects which consist of multiple regions. This modeling is used to substitute for the class priors in a Bayes segmentation setup, in which the class conditional probabilities are estimated using the MF-based SVM algorithm also. Experiments with multispectral remote sensing data show that the proposed segmentation approach is more accurate and robust than other known alternatives.
Cerebral white matter segmentation from MRI using probabilistic graph cuts and geometric shape priors

Study of cerebral white matter in the brain is an important medical problem which helps in better understanding of brain disorders like autism. The goal of this research is to segment the cerebral white matter from the input Magnetic Resonance Imaging (MRI) data. The present segmentation problem becomes extremely difficult due to i) the complex shape of the cerebral white matter and ii) the very low contrast between the white matter and the surrounding structures in the MRI data. We employ a novel probabilistic graph cut algorithm, where the edge capacity functions of the classical graph cut algorithm are modified according to the probabilities of pixels to belong to different segmentation classes. In order to separate the surrounding structures from the white matter, two appropriate geometric shape priors are introduced. Experimentation in 2D with 20 different datasets has yielded an average segmentation accuracy of 94.78%.
Image-based detection of Corpus Callosum variability for more accurate discrimination between dyslexic and normal brains

Dyslexia severely impairs learning abilities of children, so that improved diagnostic methods are needed. Neuropathological studies have revealed an abnormal anatomy of the Corpus Callosum (CC) in dyslexic brains. We propose a new approach to quantitative analysis of three-dimensional (3D) magnetic resonance images (MRI) of the brain that ensures a more accurate quantification of anatomical differences between the CC of dyslexic and normal subjects. It consists of three main processing steps: (i) segmenting the CC from a given 3D MRI using the learned CC shape and visual appearance; (ii) extracting a centerline of the CC; and (iii) cylindrical mapping of the CC surface for its comparative analysis. Our experiments revealed significant differences (at the 95% confidence level) between 14 normal and 16 dyslexic subjects in four anatomical divisions, i.e. splenium, rostrum, genu and body of their CCs.
Automatic analysis of left ventricle wall thickness using short-axis cine CMR images

A new automatic framework for analyzing wall thickness and thickening function on short-axis cine cardiac magnetic resonance (CMR) images is proposed. Inner and outer wall borders (contours) are segmented in a CMR image with a level set deformable model. Its evolution is controlled by a stochastic speed function that accounts for an “object-background” Markov-Gibbs shape and appearance model. Found by solving a Laplace equation, point-to-point correspondences between the inner and outer borders provide initial estimates of the local wall thickness and thickening function index. Effects of segmentation errors are reduced and a 3-D continuity analysis of the left ventricle (LV) wall thickening values is performed by using the maximum a posteriori (MAP) estimates for a pairwise energy function of a generalized Gauss-Markov random field (GGMRF) probabilistic model. Experiments with in vivo CMR data confirm the robustness and accuracy of the proposed framework.
A new NMF-autoencoder based CAD system for early diagnosis of prostate cancer

In this paper, we propose a novel non-invasive framework for the early diagnosis of prostate cancer from diffusion-weighted magnetic reasoning imaging (DW-MRI). The proposed approach consists of three main steps. In the first step, the prostate is localized and segmented based on a new level-set model. This model is guided by a stochastic speed function that is derived using nonnegative matrix factorization (NMF). The NMF attributes are calculated using information from the MRI intensity, a probabilistic shape model, and the spatial interactions between prostate voxels. In the second step, the apparent diffusion coefficient (ADC) of the segmented prostate volume is mathematically calculated for different b-values. To preserve continuity, the calculated ADC values are normalized and refined using a Generalized Gauss-Markov Random Field (GGMRF) image model. The cumulative distribution function (CDF) of refined ADC for the prostate tissues at different b-values are then constructed. These CDFs are considered as global features which can be used to distinguish between benign and malignant tumors. Finally, a deep learning auto-encoder network, trained by a non-negativity constraint algorithm (NCAE), is used to classify the prostate tumor as benign or malignant based on the CDFs extracted from the previous step. Preliminary experiments on 42 clinical DW-MRI data sets resulted in 97.6% correct classification (sensitivity = 100% and specificity = 95.24%), indicating the high accuracy of the proposed framework.