nils-herrmann/clean_scirepeval huggingface dataset:
3-D active appearance models: segmentation of cardiac MR and ultrasound images

A model-based method for three-dimensional image segmentation was developed and its performance assessed in segmentation of volumetric cardiac magnetic resonance (MR) images and echocardiographic temporal image sequences. Comprehensive design of a three-dimensional (3-D) active appearance model (AAM) is reported for the first time as an involved extension of the AAM framework introduced by Cootes et al. The model's behavior is learned from manually traced segmentation examples during an automated training stage. Information about shape and image appearance of the cardiac structures is contained in a single model. This ensures a spatially and/or temporally consistent segmentation of three-dimensional cardiac images. The clinical potential of the 3-D AAM is demonstrated in short-axis cardiac MR images and four-chamber echocardiographic sequences. The method's performance was assessed by comparison with manually identified independent standards in 56 clinical MR and 64 clinical echo image sequences. The AAM method showed good agreement with the independent standard using quantitative indexes of border positioning errors, endo- and epicardial volumes, and left ventricular mass. In MR, the endocardial volumes, epicardial volumes, and left ventricular wall mass correlation coefficients between manual and AAM were R/sup 2/=0.94,0.97,0.82, respectively. For echocardiographic analysis, the area correlation was R/sup 2/=0.79. The AAM method shows high promise for successful application to MR and echocardiographic image analysis in a clinical setting.
Automated contour detection in X-ray left ventricular angiograms using multiview active appearance models and dynamic programming

This paper describes a new approach to the automated segmentation of X-ray left ventricular (LV) angiograms, based on active appearance models (AAMs) and dynamic programming. A coupling of shape and texture information between the end-diastolic (ED) and end-systolic (ES) frame was achieved by constructing a multiview AAM. Over-constraining of the model was compensated for by employing dynamic programming, integrating both intensity and motion features in the cost function. Two applications are compared: a semi-automatic method with manual model initialization, and a fully automatic algorithm. The first proved to be highly robust and accurate, demonstrating high clinical relevance. Based on experiments involving 70 patient data sets, the algorithm's success rate was 100% for ED and 99% for ES, with average unsigned border positioning errors of 0.68 mm for ED and 1.45 mm for ES. Calculated volumes were accurate and unbiased. The fully automatic algorithm, with intrinsically less user interaction was less robust, but showed a high potential, mostly due to a controlled gradient descent in updating the model parameters. The success rate of the fully automatic method was 91% for ED and 83% for ES, with average unsigned border positioning errors of 0.79 mm for ED and 1.55 mm for ES
Atlas-based articulated skeleton segmentation of μSPECT mouse data

In this paper we propose an automated articulated atlas-based approach for bone segmentation in whole-body μSPECT data of mice, obtained by injecting the 99mTc-methylene diphosphonate (99mTc - MDP). This is a difficult task, since SPECT data is usually noisy and low resolution, and the skeleton image is incomplete with several portions missing (e.g.: in limbs and skull). For this purpose the articulated version of the MOBY atlas skeleton with a correspondent hierarchical tree description is used. Iterative Closest Point registration is used, while constraining the local degrees of freedom (DoFs) in accordance to the type of joint and its range of motion. The method was tested using 3 whole-body μSPECT mouse datasets acquired using a NanoSPECT/CT scanner for small animals and the MOBY atlas. To evaluate the proposed algorithm, manual bone segmentations of extracted skeletons from the correspondent CT datasets were used. Euclidean point to surface distances for each dataset and the MOBY atlas were calculated. The obtained results indicate that after registration, the mean Euclidean distance descreased from 8.37 ± 8.70 to 2.27 ± 2.06 voxels. The results were presented using a novel method for change visualization in small animal imaging (Articulated Planar Reformation).
Automatic segmentation of echocardiographic sequences by active appearance motion models

A novel extension of active appearance models (AAMs) for automated border detection in echocardiographic image sequences is reported. The active appearance motion model (AAMM) technique allows fully automated robust and time-continuous delineation of left ventricular (LV) endocardial contours over the full heart cycle with good results. Nonlinear intensity normalization was developed and employed to accommodate ultrasound-specific intensity distributions. The method was trained and tested on 16-frame phase-normalized transthoracic four-chamber sequences of 129 unselected infarct patients, split randomly into a training set (n=65) and a test set (n=64). Borders were compared to expert drawn endocardial contours. On the test set, fully automated AAMM performed well in 97% of the cases (average distance between manual and automatic landmark points was 3.3 mm, comparable to human interobserver variabilities). The ultrasound-specific intensity normalization proved to be of great value for good results in echocardiograms. The AAMM was significantly more accurate than an equivalent set of two-dimensional AAMs.
A neuro-fuzzy system for automatic assessment of myocardial viability in positron emission tomography

A critical aspect of the treatment of patients with heart failure is the ability to predict the success of a revascularisation procedure. This prediction is based on the assessment of myocardial viability. Positron emission tomography is considered to be the gold standard for myocardial viability studies. Most of these studies are still based on qualitative assessment of the extent and severity of the disease. More recent studies quantify the extent of viable tissue manually using interactive software. In this work an accurate automatic assessment method is presented. Our approach is based on neuro-fuzzy techniques. A self organized radial basis function network has been implemented for image segmentation and parameter extraction, and an adaptive network-based fuzzy inference system is used to combine complementary information (myocardial perfusion and metabolism) to decide on myocardial viability. This work demonstrates the efficiency and accuracy of neuro-fuzzy techniques when carefully applied to viability assessment. It is an innovative approach to viability parametric image construction.
2D/3D registration of micro-CT data to multi-view photographs based on a 3D distance map

In this work we present a method for registration of a CT-derived mouse skin surface to two or more 2D, geometrically calibrated, photographs of the same animal using a similarity transformation model. We show that by using a 3D distance map, which is reconstructed from the animal skin silhouettes in the 2D photographs, and by penalizing large angle differences between distance map gradients and CT-based skin surface normals, we are able to construct a registration criterion that is robust to silhouette outliers and yields accurate results for synthetic and real data (mean skin surface distance 0.12mm and 1.35mm respectively).
Model-based alignment of Look-Locker MRI sequences for calibrated myocardical scar tissue quantification

The characterization of myocardial scar tissue in Late Gadolinium Enhancement (LGE) MRI volumes is hampered by the nonquantitative nature of MRI image intensities. Using the widely available Look-Locker (LL) sequence that images the heart using different inversion times a T1 map can be created per patient to calibrate the LGE datasets. However, due to the nature of the LL acquisition, the myocardium is imaged at different phases of the cardiac cycle, resulting in deformations between slices of the LL stack and preventing accurate T1 map estimates. In this paper a method is proposed for the non-rigid alignment of the LL stack that uses a model of the exponential contrast development throughout the LL stack to concurrently align all LL stack slices. The model based alignment is shown to be more robust than a pairwise mutual information based alignment. More importantly, correlations between the relaxivity (R1) map and the LGE intensities (needed for the LGE calibration) are higher using the proposed alignment than when using manual annotations. The model based alignment thereby allows the use of the LL sequence for LGE calibration without manually annotating the (typically) 33 slices in this sequence. After alignment, the myocardium only needs to be annotated in the LGE slice. The latter is also needed for non-calibrated scar quantification and thus requires no additional user effort.
Confidence of model based shape reconstruction from sparse data

Statistical shape models (SSM) are commonly applied for plausible interpolation of missing data in medical imaging. However, when fitting a shape model to sparse information, many solutions may fit the available data. In this paper we derive a constrained SSM to fit noisy sparse input landmarks and assign a confidence value to the resulting reconstructed shape. An evaluation study is performed to compare three methods used for sparse SSM fitting w.r.t. specificity, generalization ability, and correctness of estimated confidence limits with an increasing amount of input information. We find that the proposed constrained shape model outperforms the other models, is robust against the selection and amount of sparse information, and indicates the shape confidence well.
Regression-Based Cardiac Motion Prediction From Single-Phase CTA

State of the art cardiac computed tomography (CT) enables the acquisition of imaging data of the heart over the entire cardiac cycle at concurrent high spatial and temporal resolution. However, in clinical practice, acquisition is increasingly limited to 3-D images. Estimating the shape of the cardiac structures throughout the entire cardiac cycle from a 3-D image is therefore useful in applications such as the alignment of preoperative computed tomography angiography (CTA) to intra-operative X-ray images for improved guidance in coronary interventions. We hypothesize that the motion of the heart is partially explained by its shape and therefore investigate the use of three regression methods for motion estimation from single-phase shape information. Quantitative evaluation on 150 4-D CTA images showed a small, but statistically significant, increase in the accuracy of the predicted shape sequences when using any of the regression methods, compared to shape-independent motion prediction by application of the mean motion. The best results were achieved using principal component regression resulting in point-to-point errors of 2.3±0.5 mm, compared to values of 2.7±0.6 mm for shape-independent motion estimation. Finally, we showed that this significant difference withstands small variations in important parameter settings of the landmarking procedure.
A 3-D Active Shape Model Driven by Fuzzy Inference: Application to Cardiac CT and MR

Manual quantitative analysis of cardiac left ventricular function using multislice CT and MR is arduous because of the large data volume. In this paper, we present a 3-D active shape model (ASM) for semiautomatic segmentation of cardiac CT and MR volumes, without the requirement of retraining the underlying statistical shape model. A fuzzy c-means based fuzzy inference system was incorporated into the model. Thus, relative gray-level differences instead of absolute gray values were used for classification of 3-D regions of interest (ROIs), removing the necessity of training different models for different modalities/acquisition protocols. The 3-D ASM was evaluated using 25 CT and 15 MR datasets. Automatically generated contours were compared to expert contours in 100 locations. For CT, 82.4% of epicardial contours and 74.1% of endocardial contours had a maximum error of 5 mm along 95% of the contour arc length. For MR, those numbers were 93.2% (epicardium) and 91.4% (endocardium). Volume regression analysis revealed good linear correlations between manual and semiautomatic volumes, r 2 ges 0.98. This study shows that the fuzzy inference 3-D ASM is a robust promising instrument for semiautomatic cardiac left ventricle segmentation. Without retraining its statistical shape component, it is applicable to routinely acquired CT and MR studies.
Identification of cellular dynamic patterns resulting from repetitive photobleaching using Independent Component Analysis

Fluorescence Loss In Photobleaching (FLIP) experiments are used to determine the dynamic properties of fluorescently labeled macromolecules in cells. This paper presents a novel method to facilitate the interpretation of dynamic patterns in FLIP experiments based on Independent Component Analysis. By decomposing the image sequence into a stationary and a dynamic component, and by deriving a bleaching contrast function, we demonstrate that the triplet of component and bleaching images enhances the interpretation of FLIP sequences by outlining the movement of two fluorescent proteins with well defined dynamic characteristics.
CyteGuide: Visual Guidance for Hierarchical Single-Cell Analysis

Single-cell analysis through mass cytometry has become an increasingly important tool for immunologists to study the immune system in health and disease. Mass cytometry creates a high-dimensional description vector for single cells by time-of-flight measurement. Recently, t-Distributed Stochastic Neighborhood Embedding (t-SNE) has emerged as one of the state-of-the-art techniques for the visualization and exploration of single-cell data. Ever increasing amounts of data lead to the adoption of Hierarchical Stochastic Neighborhood Embedding (HSNE), enabling the hierarchical representation of the data. Here, the hierarchy is explored selectively by the analyst, who can request more and more detail in areas of interest. Such hierarchies are usually explored by visualizing disconnected plots of selections in different levels of the hierarchy. This poses problems for navigation, by imposing a high cognitive load on the analyst. In this work, we present an interactive summary-visualization to tackle this problem. CyteGuide guides the analyst through the exploration of hierarchically represented single-cell data, and provides a complete overview of the current state of the analysis. We conducted a two-phase user study with domain experts that use HSNE for data exploration. We first studied their problems with their current workflow using HSNE and the requirements to ease this workflow in a field study. These requirements have been the basis for our visual design. In the second phase, we verified our proposed solution in a user evaluation.
FULLY AUTOMATED WHOLE-BODY REGISTRATION IN MICE USING AN ARTICULATED SKELETON ATLAS

In this paper, we propose a fully automated articulated registration approach for whole-body 3D data of mice. The method is based on a hierarchical anatomical model of the skeletal system where we specified position and degrees of freedom for each joint. Model fitting is performed by traversing a hierarchical part-tree, which enables a coarse-to-fine registration from the inner articulations outwards. The method was tested on 12 micro-CT volumes, giving accurate alignment of the skeletal structures in all cases.
Articulated Planar Reformation for Change Visualization in Small Animal Imaging

The analysis of multi-timepoint whole-body small animal CT data is greatly complicated by the varying posture of the subject at different timepoints. Due to these variations, correctly relating and comparing corresponding regions of interest is challenging.In addition, occlusion may prevent effective visualization of these regions of interest. To address these problems, we have developed a method that fully automatically maps the data to a standardized layout of sub-volumes, based on an articulated atlas registration.We have dubbed this process articulated planar reformation, or APR. A sub-volume can be interactively selected for closer inspection and can be compared with the corresponding sub-volume at the other timepoints, employing a number of different comparative visualization approaches. We provide an additional tool that highlights possibly interesting areas based on the change of bone density between timepoints. Furthermore we allow visualization of the local registration error, to give an indication of the accuracy of the registration. We have evaluated our approach on a case that exhibits cancer-induced bone resorption.
Exploring Models and Data for Remote Sensing Image Caption Generation

Inspired by recent development of artificial satellite, remote sensing images have attracted extensive attention. Recently, notable progress has been made in scene classification and target detection. However, it is still not clear how to describe the remote sensing image content with accurate and concise sentences. In this paper, we investigate to describe the remote sensing images with accurate and flexible sentences. First, some annotated instructions are presented to better describe the remote sensing images considering the special characteristics of remote sensing images. Second, in order to exhaustively exploit the contents of remote sensing images, a large-scale aerial image data set is constructed for remote sensing image caption. Finally, a comprehensive review is presented on the proposed data set to fully advance the task of remote sensing caption. Extensive experiments on the proposed data set demonstrate that the content of the remote sensing image can be completely described by generating language descriptions. The data set is available at https://github.com/201528014227051/RSICD_optimal.
A Unified Learning Framework for Single Image Super-Resolution

It has been widely acknowledged that learning- and reconstruction-based super-resolution (SR) methods are effective to generate a high-resolution (HR) image from a single low-resolution (LR) input. However, learning-based methods are prone to introduce unexpected details into resultant HR images. Although reconstruction-based methods do not generate obvious artifacts, they tend to blur fine details and end up with unnatural results. In this paper, we propose a new SR framework that seamlessly integrates learning- and reconstruction-based methods for single image SR to: 1) avoid unexpected artifacts introduced by learning-based SR and 2) restore the missing high-frequency details smoothed by reconstruction-based SR. This integrated framework learns a single dictionary from the LR input instead of from external images to hallucinate details, embeds nonlocal means filter in the reconstruction-based SR to enhance edges and suppress artifacts, and gradually magnifies the LR input to the desired high-quality SR result. We demonstrate both visually and quantitatively that the proposed framework produces better results than previous methods from the literature.
Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression

Semisupervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample. This paper proposes an effective approach of semisupervised dimensionality reduction through label propagation and label regression. Different from previous efforts, the new approach propagates the label information from labeled to unlabeled data with a well-designed mechanism of random walks, in which outliers are effectively detected and the obtained virtual labels of unlabeled data can be well encoded in a weighted regression model. These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction. By this means, when the manifold or the clustering assumption of data is satisfied, the labels of labeled data can be correctly propagated to the unlabeled data; and thus, the proposed approach utilizes the labeled and the unlabeled data more effectively than previous work. Experimental results are carried out upon several databases, and the advantage of the new approach is well demonstrated.
Harmonious Genetic Clustering

To automatically determine the number of clusters and generate more quality clusters while clustering data samples, we propose a harmonious genetic clustering algorithm, named HGCA, which is based on harmonious mating in eugenic theory. Different from extant genetic clustering methods that only use fitness, HGCA aims to select the most suitable mate for each chromosome and takes into account chromosomes gender, age, and fitness when computing mating attractiveness. To avoid illegal mating, we design three mating prohibition schemes, i.e., no mating prohibition, mating prohibition based on lineal relativeness, and mating prohibition based on collateral relativeness, and three mating strategies, i.e., greedy eugenics-based mating strategy, eugenics-based mating strategy based on weighted bipartite matching, and eugenics-based mating strategy based on unweighted bipartite matching, for harmonious mating. In particular, a novel single-point crossover operator called variable-length-and-gender-balance crossover is devised to probabilistically guarantee the balance between population gender ratio and dynamics of chromosome lengths. We evaluate the proposed approach on real-life and artificial datasets, and the results show that our algorithm outperforms existing genetic clustering methods in terms of robustness, efficiency, and effectiveness.
Supervised tensor learning

This paper aims to take general tensors as inputs for supervised learning. A supervised tensor learning (STL) framework is established for convex optimization based learning techniques such as support vector machines (SVM) and minimax probability machines (MPM). Within the STL framework, many conventional learning machines can be generalized to take n/sup th/-order tensors as inputs. We also study the applications of tensors to learning machine design and feature extraction by linear discriminant analysis (LDA). Our method for tensor based feature extraction is named the tenor rank-one discriminant analysis (TR1DA). These generalized algorithms have several advantages: 1) reduce the curse of dimension problem in machine learning and data mining; 2) avoid the failure to converge; and 3) achieve better separation between the different categories of samples. As an example, we generalize MPM to its STL version, which is named the tensor MPM (TMPM). TMPM learns a series of tensor projections iteratively. It is then evaluated against the original MPM. Our experiments on a binary classification problem show that TMPM significantly outperforms the original MPM.