nils-herrmann/clean_scirepeval huggingface dataset:
Image-based detection of Corpus Callosum variability for more accurate discrimination between autistic and normal brains

The importance of accurate early diagnostics of autism that severely affects personal behavior and communication skills cannot be overstated. Neuropathological studies have revealed an abnormal anatomy of the Corpus Callosum (CC) in autistic 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 autistic 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 17 normal and 17 autistic subjects in four anatomical divisions, i.e. splenium, rostrum, genu and body of their CC.
A new CNN-based system for early diagnosis of prostate cancer

We propose a convolutional neural network (CNN) based computer-aided diagnosis (CAD) system for early diagnosis of prostate cancer from diffusion-weighted magnetic resonance imaging (DWI). The proposed CNN-based CAD system begins by segmenting the prostate in a DWI dataset. Segmentation is achieved using our previously developed approach based on a geometric deformable model whose evolution is guided by first- and second-order appearance models. The spatial maps of apparent diffusion coefficients (ADCs) within the prostate, calculated for each 6-value, are used as image-based markers for the blood diffusion of the scanned prostate. For the purpose of classification/diagnosis, a three dimensional CNN has been trained to exact the most discriminatory features of these ADC maps for distinguishing malignant from benign prostate tumors. The proposed CNN-based CAD system is tested on DWI acquired from 23 patients using seven distinct 6-values. These experiments on in-vivo data confirm the high accuracy of the proposed CNN-based CAD system compared with our previously published results.
A new approach for automatic analysis of 3D low dose CT images for accurate monitoring the detected lung nodules

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 monitoring the development of lung nodules detected in successive chest low dose (LD) CT scans of a patient. We propose a new methodology for 3D LDCT data registration which is non-rigid and involves two steps: (i) global alignment of one scan (target) to another scan (reference or prototype) using the learned prior appearance model followed by (ii) local alignment in order to correct for intricate deformations. After equalizing signals for two subsequent chest scans, visual appearance of these chest images is modeled with a Markov-Gibbs random field with pairwise interaction. We estimate the affine transformation that globally register the target to the prototype by gradient descent maximization of a special Gibbs energy function. To handle local deformations, we deform each voxel of the target over evolving closed 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 that minimize distances between the corresponding voxel pairs on the iso-surfaces in both the data sets. Preliminary results on the 135 LDCT data sets from 27 patients show that our proper registration could lead to precise diagnosis and identification of the development of the detected pulmonary nodules.
A novel MRA framework based on integrated global and local analysis for accurate segmentation of the cerebral vascular system

A novel, fully automated segmentation framework that can extract cerebral blood vessels larger than 1 mm diameter from time-offlight magnetic resonance angiography (TOF-MRA) scans was developed. The segmentation framework consists of the following main steps: 1) Following bias correction, the TOF-MRA image is modeled as a a three-dimensional (3-D) generalized Gauss-Markov random field (GGMRF) where pairwise interactions between the 26-neighborhood voxels enhance the homogeneity of brain and vascular tissues while preserving the 3-D edges between them. 2) To automatically extract the region of interest, a novel approach was developed to remove the skull from TOF-MRA images based on the integration of the Markov-Gibbs random field (MGRF) model of TOF-MRA visual appearance with a geometric deformable model (brain isosurface) that preserves brain topology during the extraction process. 3) The initial vascular system is extracted using Bayes classifier following the estimation of the marginal probability density of TOF-MRA voxel values for both blood vessels and other brain tissues, modeled as a linear combination of discrete Gaussians (LCDG). 4) To enhance the extraction of small vessels, a seed-generation refinement algorithm was used to identify points within regions likely to contain small vessels overlooked by the previous step. 5) Finally, a 3-D region-growing connected components algorithm was applied to extract the final, connected vascular tree. The proposed approach was tested on in-vivo data (200 TOF-MRA data sets), and qualitatively and quantitatively validated by an MRA expert (ground truth). The proposed approach resulted in high segmentation accuracy with 86% Dice similarity coefficient, and 10% absolute volume difference.
Image segmentation with a parametric deformable model using shape and appearance priors

We propose a novel parametric deformable model controlled by shape and visual appearance priors learned from a training subset of co-aligned images of goal objects. The shape prior is derived from a linear combination of vectors of distances between the training boundaries and their common centroid. The appearance prior considers gray levels within each training boundary as a sample of a Markov-Gibbs random field with pairwise interaction. Spatially homogeneous interaction geometry and Gibbs potentials are analytically estimated from the training data. To accurately separate a goal object from an arbitrary background, empirical marginal gray level distributions inside and outside of the boundary are modeled with adaptive linear combinations of discrete Gaussians (LCDG). The evolution of the parametric deformable model is based on solving an Eikonal partial differential equation with a new speed function which combines the prior shape, prior appearance, and current appearance models. Due to the analytical shape and appearance priors and a simple Expectation-Maximization procedure for getting the object and background LCDG, our segmentation is considerably faster than most of the known geometric and parametric models. Experiments with various goal images confirm the robustness, accuracy, and speed of our approach.
Alzheimer's disease diagnostics by adaptation of 3D convolutional network

Early diagnosis, playing an important role in preventing progress and treating the Alzheimer's disease (AD), is based on classification of features extracted from brain images. The features have to accurately capture main AD-related variations of anatomical brain structures, such as, e.g., ventricles size, hippocampus shape, cortical thickness, and brain volume. This paper proposed to predict the AD with a deep 3D convolutional neural network (3D-CNN), which can learn generic features capturing AD biomarkers and adapt to different domain datasets. The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans. Fully connected upper layers of the 3D-CNN are then fine-tuned for each task-specific AD classification. Experiments on the CADDementia MRI dataset with no skull-stripping preprocessing have shown our 3D-CNN outperforms several conventional classifiers by accuracy. Abilities of the 3D-CNN to generalize the features learnt and adapt to other domains have been validated on the ADNI dataset.
A novel CAD system for local and global early diagnosis of Alzheimer's disease based on PIB-PET scans

This manuscript presents a Computer Aided Diagnosis (CAD) system to assist in the early diagnosis of Alzheimer's disease (AD) with the ability to provide a personalized diagnosis by visualizing the detected abnormality per brain regions (AAL atlas). The CAD system utilizes PiB-PET scans and consists of the following four essential stages-(1) a preprocessing step performs data reorientation, co-registration, and spatial normalization; (2) partitioning the brain into 116 labeled regions utilizing a brain atlas to facilitate local diagnosis; (3) extraction of features within each region using scale-invariant Laplacian of Gaussian (LoG) that detects the maximum or minimum of a radially symmetric intensity distribution; and (4) construction of two diagnosis layers (local and global) using a Support Vector Machine (SVM) classifier and its probabilistic variant (pSVM). The CAD system was tested on 84 PiB-PET scans (19 normal control (NC) and 65 mild cognitive impairment (MCI)) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The proposed system had a classification accuracy, sensitivity, and specificity of 100%.
3-D Lung Segmentation by Incremental Constrained Nonnegative Matrix Factorization

Accurate lung segmentation from large-size 3-D chest-computed tomography images is crucial for computer-assisted cancer diagnostics. To efficiently segment a 3-D lung, we extract voxel-wise features of spatial image contexts by unsupervised learning with a proposed incremental constrained nonnegative matrix factorization (ICNMF). The method applies smoothness constraints to learn the features, which are more robust to lung tissue inhomogeneities, and thus, help to better segment internal lung pathologies than the known state-of-the-art techniques. Compared to the latter, the ICNMF depends less on the domain expert knowledge and is more easily tuned due to only a few control parameters. Also, the proposed slice-wise incremental learning with due regard for interslice signal dependencies decreases the computational complexity of the NMF-based segmentation and is scalable to very large 3-D lung images. The method is quantitatively validated on simulated realistic lung phantoms that mimic different lung pathologies (seven datasets), in vivo datasets for 17 subjects, and 55 datasets from the Lobe and Lung Analysis 2011 (LOLA11) study. For the in vivo data, the accuracy of our segmentation w.r.t. the ground truth is 0.96 by the Dice similarity coefficient, 9.0 mm by the modified Hausdorff distance, and 0.87% by the absolute lung volume difference, which is significantly better than for the NMF-based segmentation. In spite of not being designed for lungs with severe pathologies and of no agreement between radiologists on the ground truth in such cases, the ICNMF with its total accuracy of 0.965 was ranked fifth among all others in the LOLA11. After excluding the nine too pathological cases from the LOLA11 dataset, the ICNMF accuracy increased to 0.986.
3D shape analysis of the brain cortex with application to autism

To discriminate more accurately between autistic and normal brains, we detect the brain cortex variability using a spherical harmonic analysis that represents a 3D surface supported by the unit sphere with a linear combination of special basis functions, called spherical harmonics (SHs). The proposed 3D shape analysis is carried out in five steps: (i) 3D brain cortex segmentation, with a deformable 3D boundary, controlled by two probabilistic visual appearance models (the learned prior and the estimated current appearance one); (ii) 3D Delaunay triangulation to construct a 3D mesh model of the brain cortex surface; (iii) mapping this model to the unit sphere; (iv) computing the SHs for the surface; and (v) determining the number of the SHs to delineate the brain cortex. We describe the brain shape complexity with a new shape index, the estimated number of the SHs, and use it for K-nearest classification of normal and autistic brains. Initial experiments suggest that our shape index is a promising supplement to the current autism diagnostic techniques.
A new 3D automatic segmentation framework for accurate extraction of prostate from diffusion 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 the Posteriori (MAP) estimate of a new log-likelihood function that consists of three descriptors: (i) 1st-order visual appearance descriptors of the Diffusion-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 Diffusion-MRI 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 real in vivo prostate Diffusion-MRI confirm the robustness and accuracy of the proposed approach.
Robust image segmentation using learned priors

A novel parametric deformable model of a goal object controlled by shape and appearance priors learned from co-aligned training images is introduced. The shape prior is built in a linear space of vectors of distances to the training boundaries from their common centroid. The appearance prior is modeled with a spatially homogeneous 2nd-order Markov-Gibbs random field (MGRF) of gray levels within each training boundary. Geometric structure of the MGRF and Gibbs potentials are analytically estimated from the training data. To accurately separate goal objects from arbitrary background, the deformable model is evolved by solving an Eikonal partial differential equation with a speed function combining the shape and appearance priors and the current appearance model. The latter represents empirical gray level marginals inside and outside an evolving boundary with adaptive linear combinations of discrete Gaussians (LCDG). The analytical shape and appearance priors and a simple Expectation-Maximization procedure for getting the object and background LCDGs, make our segmentation considerably faster than most of the known counterparts. Experiments with various images confirm robustness, accuracy, and speed of our approach.
A level set-based framework for 3D kidney segmentation from diffusion MR images

Developing any non-invasive computer-aided diagnostic (CAD) system for the diagnosis of kidney diseases essentially requires the extraction of the kidney from medical images. We propose a shape based level-set framework for 3D kidney segmentation from diffusion-weighted magnetic resonance imaging (DW-MRI). A stochastic speed relationship is used to control the deformable model evolutions. This speed relationship is based on an adaptive shape prior guided by the first- and second-order visual appearance features of the DW-MRI data. These pre-mentioned image features are integrated into a joint Markov-Gibbs random field (MGRF) model of the kidney and its background. DW-MRI data sets from eight subjects acquired at different b-values ranging from 0 to 1000 s/mm2 are tested using a leave-one-subject-out method to evaluate the proposed segmentation approach, and to compare its performance with other segmentation methods using three evaluation metrics: the Dice similarity coefficient (DSC), the 95-percentile modified Hausdorff distance, and the absolute kidney volume difference. Robustness and accuracy of the proposed approach are confirmed through the experimental results' evaluation between manually drawn and automatically segmented contours.
A new nonrigid registration approach for motion correction of cardiac first-pass perfusion MRI

Accurate registration of cardiac first-pass magnetic resonance imaging (FP-MRI) is fundamental for precise analysis of myocardial perfusion. In this paper, we introduce and validate a new framework for accurate registration of the segmented left ventricle (LV) wall on cardiac FP-MRI. Due to the continuous physiological motion of the heart that causes the LV wall to change shape significantly and to move within and through the image plane, we developed a new methodology for 2D FP-MRI nonrigid registration that includes: (i) global target-to-reference frame-to-frame alignment based on the maximization of the normalized mutual information (NMI); (ii) local alignment based on using a B-splines transformation model that maximizes a similarity function that accounts for 1st- and 2nd-order NMI between the globally aligned frames followed by (iii) a refinement step that is based on deforming each pixel of the target wall over evolving closed equi-spaced contours (iso-contours) to closely match the reference wall. Respective iso-contours in both reference and target frames are matched based on solving the Laplace equation. We have tested our framework on both synthetic phantoms and 20 in-vivo data sets that have been collected from patients with ischemic damage from heart attacks, who are undergoing a novel myoregeneration therapy.
MRI-based diagnostic system for early detection of prostate cancer

MR image-based modalities, such as T2-weighted MR, Diffusion-weighted (DWI), and dynamic contrast-enhanced imaging (DCE-MRI) have been widely employed for early detection of prostate cancer in vivo. In this paper, we evaluate the diagnostic ability of diffusion weighted imaging (DWI) and dynamic contrast-enhanced imaging (DCE-MRI) for the detection of prostate cancer at early stage. This study included 28 patients (17 malignant and 11 benign) with biopsy proven prostate cancer. The diagnostic performance was calculated for DCE-MRI and DWI. Both DWI and DCE-MRI demonstrate 100% classification accuracy. These preliminary diagnostic results show the promise of using DWI as a supplement of DCE-MRI for early diagnosis of prostate cancer.
A novel approach for accurate estimation of left ventricle global indexes from short-axis cine MRI

A new automatic approach for the estimation of global indexes from short-axis cine cardiac magnetic resonance (CMR) images is proposed. The inner contour of the left ventricle (LV) is segmented with a level set-based deformable model. Its evolution is controlled by a specially designed stochastic speed function that accounts for a learned spatially variant statistical shape prior, a 1st-order visual appearance descriptor of the contour interior and exterior (associated with the object and background, respectively), and a spatially invariant 2nd-order homogeneity descriptor. After the inner contour delineation, the total cavity volume (time varying LV volume) data is used to estimate the LV global functional indexes, i.e., ejection fraction, systolic and diastolic slopes. Experiments with in-vivo CMR data, obtained from subjects with chronic ischemic heart disease and damage that is documented by viability MRI, confirm a high robustness and accuracy of the proposed approach.
A new framework for incorporating appearance and shape features of lung nodules for precise diagnosis of lung cancer

This paper proposes a novel framework for the classification of lung nodules using computed tomography (CT) scans. The proposed framework is based on the integrating the following features to get accurate diagnosis of detected lung nodules: (i) Spherical Harmonics-based shape features that have the ability to describe the shape complexity of the lung nodules; (ii) Higher-Order Markov Gibbs Random Field (MGRF)-based appearance model that has the ability to describe the spatial inhomogeneities in the lung nodule; and (iii) volumetric features that describe the size of lung nodules. To accurately model the surface/shape of the detect lung nodules, we used spherical harmonics expansion due to its ability to approximate the surfaces of complicated shapes. We will use the reconstruction error curve as a new metric to describe the shape complexity of the detected lung nodules. Moreover, we developed a new higher 7th-order MGRF model that has the ability to model the existing the spatial inhomogeneities for both small and large detected lung nodules. Finally, a deep autoencoder (AE) classifier is fed by the above three features to distinguish between the malignant and benign nodules. To evaluate the proposed framework, we used the publicly available data from the Lung Image Database Consortium (LIDC). We used a total of 116 nodules that were collected from 60 patients. By achieving a classification accuracy of 96.00%, the proposed system demonstrates promise to be a valuable tool for the detection of lung cancer.
Using resting state functional MRI to build a personalized autism diagnosis system

Autism spectrum disorder (ASD) is a neuro-developmental disorder that affects social functionality and communication abilities of affected subjects. Studying brain functional connectivity is believed to be one of the trending techniques used in diagnosing and understanding ASD. In this work, resting state functional MRI data for 202 (78 autistic and 1245 typically developed) subjects are used to build a novel automated deep learning based autism diagnosis system. It uses power spectral density of time courses corresponding to the spatial activation areas as raw features to be fed to a stacked autoencoder followed by probabilistic support vector machine for diagnosis. The system achieved very high accuracy, sensitivity, and specificity of 88.5%, 85.1% and 90.4% respectively. The system proved to have very good generalization ability, especially among high-risk population. In addition to its ability to differentiate between autistic and healthy controls subjects, and the global differences found between the two groups, the proposed system generates a full personalized report for each individual subject that shows the impacted areas and to what extend they are impacted. From the clinical point of view, this report could be of great value, as it helps to predict and understand autistic subjects' behavior, in addition, it helps in developing a personalized treatment plan for each subject individually, which is an important step towards personalized medicine in autism, the ultimate goal of our group.