nils-herrmann/clean_scirepeval huggingface dataset:
Multiphase level set for automated delineation of membrane-bound macromolecules

Membrane-bound macromolecules play an important role in tissue architecture and cell-cell communication, and is regulated by almost one-third of the genome. At the optical scale, one group of membrane proteins expresses themselves as linear structures along the cell surface boundaries, while others are sequestered. This paper targets the former group, whose intensity distributions are often heterogeneous and may lack specificity. Segmentation of the membrane protein enables the quantitative assessment of localization for comparative analysis. We introduce a three-step process to (i) regularize the membrane signal through iterative tangential voting, (ii) constrain the location of surface proteins by nuclear features, and (iii) assign membrane proteins to individual cells through an application of multi-phase geodesic level-set. We have validated our method against a dataset of 200 images, and demonstrated that multiphase level set has a superior performance compared to gradient vector flow snake.
Morphometric subtyping for a panel of breast cancer cell lines

A panel of cell lines of diverse molecular background offers an improved model system for high-content screening, comparative analysis, and cell systems biology. A computational pipeline has been developed to collect images from cell-based assays, segment individual cells and colonies, represent segmented objects in a multidimensional space, and cluster them for identifying distinct subpopulations. While each segmentation strategy can vary for different imaging assays, representation and subpopulation analysis share a common thread. Application of this pipeline to a library of 41 breast cancer cell lines is demonstrated. These cell lines are grown in 2D and imaged through immunofluorescence microscopy. Subpopulations in this panel are identified and shown to correlate with previous subtyping literature that was derived from transcript data.
A Delaunay triangulation approach for segmenting clumps of nuclei

Cell-based fluorescence imaging assays have the potential to generate massive amount of data, which requires detailed quantitative analysis. Often, as a result of fixation, labeled nuclei overlap and create a clump of cells. However, it is important to quantify phenotypic read out on a cell-by-cell basis. In this paper, we propose a novel method for decomposing clumps of nuclei using high-level geometric constraints that are derived from low-level features of maximum curvature computed along the contour of each clump. Points of maximum curvature are used as vertices for Delaunay triangulation (DT), which provides a set of edge hypotheses for decomposing a clump of nuclei. Each hypothesis is subsequently tested against a constraint satisfaction network for a near optimum decomposition. The proposed method is compared with other traditional techniques such as the watershed method with/without markers. The experimental results show that our approach can overcome the deficiencies of the traditional methods and is very effective in separating severely touching nuclei.
Invariant Delineation of Nuclear Architecture in Glioblastoma Multiforme for Clinical and Molecular Association

Automated analysis of whole mount tissue sections can provide insights into tumor subtypes and the underlying molecular basis of neoplasm. However, since tumor sections are collected from different laboratories, inherent technical and biological variations impede analysis for very large datasets such as The Cancer Genome Atlas (TCGA). Our objective is to characterize tumor histopathology, through the delineation of the nuclear regions, from hematoxylin and eosin (H&E) stained tissue sections. Such a representation can then be mined for intrinsic subtypes across a large dataset for prediction and molecular association. Furthermore, nuclear segmentation is formulated within a multi-reference graph framework with geodesic constraints, which enables computation of multidimensional representations, on a cell-by-cell basis, for functional enrichment and bioinformatics analysis. Here, we present a novel method, multi-reference graph cut (MRGC), for nuclear segmentation that overcomes technical variations associated with sample preparation by incorporating prior knowledge from manually annotated reference images and local image features. The proposed approach has been validated on manually annotated samples and then applied to a dataset of 377 Glioblastoma Multiforme (GBM) whole slide images from 146 patients. For the GBM cohort, multidimensional representation of the nuclear features and their organization have identified 1) statistically significant subtypes based on several morphometric indexes, 2) whether each subtype can be predictive or not, and 3) that the molecular correlates of predictive subtypes are consistent with the literature. Data and intermediaries for a number of tumor types (GBM, low grade glial, and kidney renal clear carcinoma) are available at: http://tcga.lbl.gov for correlation with TCGA molecular data. The website also provides an interface for panning and zooming of whole mount tissue sections with/without overlaid segmentation results for quality control.
Perceptual organization of radial symmetries

Radial symmetry is an important perceptual cue for the feature-based representation, fixation, and description of large-scale data sets. A new approach based on iterative voting along the gradient direction is introduced for inferring the center of mass for objects demonstrating radial symmetries that are not limited to convex geometries. The kernel topography is unique in that it votes for the most likely set of grid points where the center of mass may be located. Initially, it is applied in the direction of the gradient and then reoriented iteratively in the most probable direction. This technique can detect perceptual symmetries, has an excellent noise immunity, and is shown to be tolerant to moderate perturbation in scale. Applications of this approach to blobs with incomplete and noisy boundaries, multimedia scenes, and scientific images are demonstrated.
Classification of 3D Multicellular Organization in Phase Microscopy for High Throughput Screening of Therapeutic Targets

The current trend in high throughput screening is the utilization of more complex model systems that mimic both structural and functional properties of cellular processes in vivo. In this context, 3D cell culture models have emerged as effective systems to study tumor initiation and cancer behavior, where colony organization represents distinct phenotypic signatures that enable differentiation of cancer cells in culture using phase imaging and in the absence of clinical markers. If the colony organization can be classified into different phenotypes, it will enable rapid drug screening using phase microscopy. In this paper, we propose a novel method based on locality-constrained dictionary learning for the discrimination of aberrant colony organization in phase images, which encodes original SIFT (Scale-Invariant Feature Transform) features into high dimensional sparse codes with locality-preserving landmark points on the nonlinear manifold, and summarizes the sparse features at various locations and scales through spatial pyramid matching for robust representation. Experimental results demonstrate the significant improvement of performance, compared to the state-of-art in the field.
A constraint satisfaction network for matching 3D objects

A new approach is presented for matching visible surfaces of 3D objects using a constraint satisfaction network. This in turn provides the necessary basis for volumetric reconstruction from multiple views. By matching, the authors mean both to establish correspondence between individual faces and to compute 3D transform that would bring one in correspondence with the other. Toward this goal, constraints at three different levels of complexities are specified to produce stable and coherent assignments. The constraint satisfaction is implemented as a Hopfield network with an appropriate energy functional and minimized using simulated annealing. The system extracts objects faces by computing their bounding contours with adaptive scale space filtering. This process identifies important surface features such as jumps or occluding boundaries and creases. The pointwise feature descriptors are then linked, and an attributed graph is derived to represent the object. The nodes in the graph represent geometric surface features, and the links in the graph represent the relationship between adjacent surfaces. The authors present results on real images.<<ETX>>
Multidimensional Profiling of Cell Surface Proteins and Nuclear Markers

Cell membrane proteins play an important role in tissue architecture and cell-cell communication. We hypothesize that segmentation and multidimensional characterization of the distribution of cell membrane proteins, on a cell-by-cell basis, enable improved classification of treatment groups and identify important characteristics that can otherwise be hidden. We have developed a series of computational steps to 1) delineate cell membrane protein signals and associate them with a specific nucleus; 2) compute a coupled representation of the multiplexed DNA content with membrane proteins; 3) rank computed features associated with such a multidimensional representation; 4) visualize selected features for comparative evaluation through heatmaps; and 5) discriminate between treatment groups in an optimal fashion. The novelty of our method is in the segmentation of the membrane signal and the multidimensional representation of phenotypic signature on a cell-by-cell basis. To test the utility of this method, the proposed computational steps were applied to images of cells that have been irradiated with different radiation qualities in the presence and absence of other small molecules. These samples are labeled for their DNA content and E-cadherin membrane proteins. We demonstrate that multidimensional representations of cell-by-cell phenotypes improve predictive and visualization capabilities among different treatment groups, and identify hidden variables.
Classification of Histology Sections via Multispectral Convolutional Sparse Coding

Image-based classification of histology sections plays an important role in predicting clinical outcomes. However this task is very challenging due to the presence of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state). In the field of biomedical imaging, for the purposes of visualization and/or quantification, different stains are typically used for different targets of interest (e.g., cellular/subcellular events), which generates multi-spectrum data (images) through various types of microscopes and, as a result, provides the possibility of learning biological-component-specific features by exploiting multispectral information. We propose a multispectral feature learning model that automatically learns a set of convolution filter banks from separate spectra to efficiently discover the intrinsic tissue morphometric signatures, based on convolutional sparse coding (CSC). The learned feature representations are then aggregated through the spatial pyramid matching framework (SPM) and finally classified using a linear SVM. The proposed system has been evaluated using two large-scale tumor cohorts, collected from The Cancer Genome Atlas (TCGA). Experimental results show that the proposed model 1) outperforms systems utilizing sparse coding for unsupervised feature learning (e.g., PSDSPM ), 2) is competitive with systems built upon features with biological prior knowledge (e.g., SMLSPM ).
Harmonic cut and regularized centroid transform for localization of subcellular structures

Two novel computational techniques, harmonic cut and regularized centroid transform, are developed for segmentation of cells and their corresponding substructures observed with an epi-fluorescence microscope. Harmonic cut detects small regions that correspond to subcellular structures. These regions also affect the accuracy of the overall segmentation. They are detected, removed, and interpolated to ensure continuity within each region. We show that interpolation within each region (subcellular compartment) is equivalent to solving the Laplace equation on a multi-connected domain with irregular boundaries. The second technique, referred to as the regularized centroid transform, aims to separate touching compartments. This is achieved by adopting a quadratic model for the shape of the object and relaxing it for final segmentation.
Learning invariant features of tumor signatures

We present a novel method for automated learning of features from unlabeled image patches for classification of tumor architecture. In contrast to previous manually-designed feature detectors (e.g., Gabor basis function), the proposed method utilizes inexpensive un-labeled data to construct features. The algorithm, also known as reconstruction independent subspace analysis, can be described as a two-layer network with non-linear responses, where the second layer represents subspace structures. The technique is applied to tissue sections for characterizing necrosis, apoptotic, and viable regions of Glioblastoma Multifrome (GBM) from TCGA dataset. Experimental results show that this method outperforms more complex expert-designed approaches. The fact that our approach learns features automatically from unlabeled data promises a wider application of self-learning strategies for tissue characterization.
Scaling Up Graph-Based Semisupervised Learning via Prototype Vector Machines

When the amount of labeled data are limited, semisupervised learning can improve the learner's performance by also using the often easily available unlabeled data. In particular, a popular approach requires the learned function to be smooth on the underlying data manifold. By approximating this manifold as a weighted graph, such graph-based techniques can often achieve state-of-the-art performance. However, their high time and space complexities make them less attractive on large data sets. In this paper, we propose to scale up graph-based semisupervised learning using a set of sparse prototypes derived from the data. These prototypes serve as a small set of data representatives, which can be used to approximate the graph-based regularizer and to control model complexity. Consequently, both training and testing become much more efficient. Moreover, when the Gaussian kernel is used to define the graph affinity, a simple and principled method to select the prototypes can be obtained. Experiments on a number of real-world data sets demonstrate encouraging performance and scaling properties of the proposed approach. It also compares favorably with models learned via ℓ1-regularization at the same level of model sparsity. These results demonstrate the efficacy of the proposed approach in producing highly parsimonious and accurate models for semisupervised learning.
High-resolution reconstruction of sparse data from dense low-resolution spatio-temporal data

A novel approach for reconstruction of sparse high-resolution data from lower-resolution dense spatio-temporal data is introduced. The basic idea is to compute the dense feature velocities from lower-resolution data and project them to the corresponding high-resolution data for computing the missing data. In this context, the basic flow equation is solved for intensity, as opposed to feature velocities at high resolution. Although the proposed technique is generic, we have applied our approach to sea surface temperature (SST) data at 18 km (low-resolution dense data) for computing the feature velocities and at 4 km (high-resolution sparse data) for interpolating the missing data. At low resolution, computation of the flow field is regularized and uses the incompressibility constraints for tracking fluid motion. At high resolution, computation of the intensity is regularized for continuity across multiple frames.
Localization of saliency through iterative voting

Saliency is an important perceptual cue that occurs at different scales of resolution. Important attributes of saliency are symmetry, continuity, and closure. Detection of these attributes is often hindered by noise, variation in scale, and incomplete information. An iterative voting method using oriented kernels is introduced for inferring saliency as it relates to symmetry or continuity. A unique aspect of the technique is in the kernel topography, which is refined and reoriented iteratively. The technique can cluster and group nonconvex perceptual circular symmetries along the radial line or sparse features along the tangential direction. It has an excellent noise immunity, and is shown to be tolerant to perturbation in scale. Applications of this approach to blobs with incomplete and noisy boundaries and to scientific images are demonstrated.
A layered network for the correspondence of 3D objects

A computational approach for solving the correspondence problem between different views of objects in range images is presented. This is modeled as a layered constraint satisfaction network which can be implemented on a parallel analog neural network. In this approach, each view of an object is represented by an attributed graph with nodes as surfaces and their bounding vertices, and links as relations between adjacent surfaces. The matching strategy is a two-step process. Each step is formulated with a constraint satisfaction network, and implemented on a Hopfield network. At each level, a set of local, adjacency and global constraints is specified, and an appropriate energy function to be minimized is defined. At the first level of this hierarchy, surface patches are matched and clusters of rotation transformations are hypothesized. At the second level, the computed rotation transformation is applied to the corresponding vertices, and the translation vector is computed.<<ETX>>
BioSig: a bioinformatic system for studying the mechanism of inter-cell signaling

Mapping inter-cell signaling pathways requires an integrated view of experimental and informatic protocols. BioSig provides the foundation of cataloging inter-cell responses as a function of particular conditioning, treatment, staining, etc. for either in vivo or in vitro experiments. This paper outlines the system architecture, a functional data model for representing experimental protocols, algorithms for image analysis, and the required statistical analysis. The architecture provides remote shared operation of an inverted optical microscope, and couples instrument operation with images acquisition and annotation. The information is stored in an object-oriented database. The algorithms extract structural information such as morphology and organization, and map it to functional information such as inter-cellular responses. An example of usage of this system is included.
Molecular bases of morphometric composition in Glioblastoma multiforme

Integrated analysis of tissue histology with the genome-wide array (e.g., OMIC) and clinical data have the potential for hypothesis generation and be prognostic. OMIC and clinical data are typically characterized and summarized at the patient level while whole mount histological sections are often heterogeneous in terms of nuclear morphology and organization. In this paper, we propose a multilevel framework for summarization and association of morphometric data. At the lowest level, each nucleus is segmented and then profiled with a multi-dimensional representation. At the intermediate level, cellular profiles are summarized within a local neighborhood, and further clustered into subtypes. At the highest level, each patient is represented by the composition of subtypes that are computed from the intermediate level, and then integrated with OMIC and outcome data for further analysis. The framework has been applied to Glioblastoma multiforme (GBM) data from The Cancer Genome Atlas (TCGA). Based on cellularity and nuclear size, four subtypes have been identified at the intermediate level. Subsequent multi-variate survival analysis indicates that the patient composition of one of the subtypes, with extremely low cellularity and small nucleus size, has a significantly higher hazard ratio. Further correlation of this subtype with the molecular data reveals enrichment of (i) STAT3 pathway and (ii) common regulators of PKC, TNF, AGT, and PDGF.
Modeling of Front Evolution with Graph Cut Optimization

In this paper, we present a novel active contour model, in which the traditional gradient descent optimization is replaced by graph cut optimization. The basic idea is to first define an energy function according to curve evolution and then construct a graph with well selected edge weights based on the objective energy function, which is further optimized via graph cut algorithm. In this fashion, our model shares advantages of both level set method and graph cut algorithm, which are "topological" invariance, computational efficiency, and immunity to being stuck in the local minima. The model is validated on synthetic images, applied to two-class segmentation problem, and compared with the traditional active contour to demonstrate effectiveness of the technique. Finally, the method is applied to samples imaged with transmission electron microscopy that demonstrate complex textured patterns corresponding subcellular regions and micro-anatomy.
A Bayesian approach for image segmentation with shape priors

Color and texture have been widely used in image segmentation; however, their performance is often hindered by scene ambiguities, overlapping objects, or missing parts. In this paper, we propose an interactive image segmentation approach with shape prior models within a Bayesian framework. Interactive features, through mouse strokes, reduce ambiguities, and the incorporation of shape priors enhances quality of the segmentation where color and/or texture are not solely adequate. The novelties of our approach are in (i) formulating the segmentation problem in a well-defined Bayesian framework with multiple shape priors, (ii) efficiently estimating parameters of the Bayesian model, and (iii) multi-object segmentation through user-specified priors. We demonstrate the effectiveness of our method on a set of natural and synthetic images.
Stacked Predictive Sparse Coding for Classification of Distinct Regions in Tumor Histopathology

Image-based classification of histology sections, in terms of distinct components (e.g., tumor, stroma, normal), provides a series of indices for tumor composition. Furthermore, aggregation of these indices, from each whole slide image (WSI) in a large cohort, can provide predictive models of the clinical outcome. However, performance of the existing techniques is hindered as a result of large technical variations and biological heterogeneities that are always present in a large cohort. We propose a system that automatically learns a series of basis functions for representing the underlying spatial distribution using stacked predictive sparse decomposition (PSD). The learned representation is then fed into the spatial pyramid matching framework (SPM) with a linear SVM classifier. The system has been evaluated for classification of (a) distinct histological components for two cohorts of tumor types, and (b) colony organization of normal and malignant cell lines in 3D cell culture models. Throughput has been increased through the utility of graphical processing unit (GPU), and evaluation indicates a superior performance results, compared with previous research.