nils-herrmann/clean_scirepeval huggingface dataset:
Iterative Tensor Voting for Perceptual Grouping of Ill-Defined Curvilinear Structures

In this paper, a novel approach is proposed for perceptual grouping and localization of ill-defined curvilinear structures. Our approach builds upon the tensor voting and the iterative voting frameworks. Its efficacy lies on iterative refinements of curvilinear structures by gradually shifting from an exploratory to an exploitative mode. Such a mode shifting is achieved by reducing the aperture of the tensor voting fields, which is shown to improve curve grouping and inference by enhancing the concentration of the votes over promising, salient structures. The proposed technique is validated on delineating adherens junctions that are imaged through fluorescence microscopy. However, the method is also applicable for screening other organisms based on characteristics of their cell wall structures. Adherens junctions maintain tissue structural integrity and cell-cell interactions. Visually, they exhibit fibrous patterns that may be diffused, heterogeneous in fluorescence intensity, or punctate and frequently perceptual. Besides the application to real data, the proposed method is compared to prior methods on synthetic and annotated real data, showing high precision rates.
CHEF: convex hull of elliptic features for 3D blob detection

We present an efficient protocol for robust detection of 3D blobs from volumetric datasets. The approach has three steps. The first step of the process detects elliptic features by classifying the Hessian of the scale space representation of the volume data. These features are then grouped into 3D connected components, which are subsequently partitioned by computing a convex hull of each connected component. The proposed framework was applied to a database of multicellular systems for detailed quantitative analysis.
High-resolution reconstruction of sparse data from dense low-resolution spatio-temporal data

An approach for reconstruction of sparse high-resolution data from lower-resolution dense spatiotemporal 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 flow field is regularized and uses the incompressibility constraints for tracking fluid motion. At high resolution, computation of intensity is regularized for continuity across multiple frames.
Declarative flow control for distributed instrumentation

We have developed a "microscopy channel" to advertise a unique set of online scientific instruments and to let users join a particular session, perform an experiment, collaborate with other users, and collect data for further analysis. The channel is a collaborative problem solving environment (CPSE) that allows for both synchronous and asynchronous collaboration, as well as flow control for enhanced scalability. The flow control is a declarative feature that enhances software functionality at the experimental scale. Our testbed includes several unique electron and optical microscopes with applications ranging from material science to cell biology. We have built a system that leverages current commercial CORBA services, Web servers, and flow control specifications to meet diverse requirements for microscopy and experimental protocols. In this context, we have defined and enhanced instrument services (IS), exchange services (ES), computational services (CS), and declarative services (DS) that sit on top of CORBA and its enabling services (naming, trading, security, and notification). IS provides a layer of abstraction for controlling any type of microscope. ES provides a common set of utilities for information management and transaction. CS provides the analytical capabilities needed for online microscopy. DS provides mechanisms for flow control for improving the dynamic behavior of the system.
PERCEPTUAL GROUPING OF MEMBRANE SIGNALS IN CELL-BASED ASSAYS

Membrane proteins organize themselves in a linear fashion where adjacent cells are attached together along the basal-lateral region. Their intensity distributions are often heterogeneous and may lack specificity. Grouping of these linear structures can aid in segmentation and quantitative representation of protein localization. However, quantitative analysis of these signals is often hindered by noise, variation in scale, and perceptual features. This paper introduces an iterative voting method for inferring the membrane signal as it relates to continuity. A unique aspect of this technique is in the topography of the voting kernel, which is refined and reoriented iteratively. The technique can cluster and group membrane signals along the tangential direction. It has an excellent noise immunity and is tolerant to perturbations in scale. Application of this technique to quantitative analysis of cell-cell adhesion mediated by integral cell membrane proteins is demonstrated
Comparison of sparse coding and kernel methods for histopathological classification of gliobastoma multiforme

This paper compares the performance of redundant representation and sparse coding against classical kernel methods for classifying histological sections. Sparse coding has been proven an effective technique for restoration, and has recently been extended to classification. The main issue with histology sections classification is inherent heterogeneity, which is a result of technical and biological variations. Technical variations originate from sample preparation, fixation, and staining from multiple laboratories, whereas biological variations originate from tissue content. Image patches are represented with invariant features at local and global scales, where local refers to responses measured with Laplacian of Gaussians, and global refers to measurements in the color space. Experiments are designed to learn dictionaries through sparse coding, and to train classifiers through kernel methods using normal, necrotic, apoptotic, and tumor regions with characteristics of high cellularity. Two different kernel methods, that of a support vector machine (SVM) and a kernel discriminant analysis (KDA), were used for comparative analysis. Preliminary investigation on the histological samples of Glioblastoma multiforme (GBM) indicates the kernel methods perform as good, if not better, than sparse coding with redundant representation.
DeepView: a channel for distributed microscopy

The paper outlines the requirements, architecture, and design of a "Microscopy Channel" over the wide area network. A microscopy channel has a listing of available online microscopes, where users can seamlessly participate in an experiment, acquire expert opinions, collect and process data, and store it in their electronic notebook. The proposed channel is a collaborative problem solving environment (PSE) that leverages existing computational toolkits to support simulation and inverse problem solving. Our testbed includes several unique electron and optical microscopes with applications ranging from material science to biology. We have studied current commercial services from OMG and concluded that three basic services are needed to meet the scalability and functionality constraints. These include: Instrument Services (IS), Exchange Services (ES), and Computational Services (CS). These services sit on top of CORBA and its enabling services (naming, trading, security, and notification). IS provide a layer of abstraction for controlling any type of microscope. ES provide a common set of utilities for information management and transaction. CS provide the analytical capabilities needed for online microscopy. The enabling services provide a mechanism for resource discovery, safety and security, and decoupled communication.
Singular features in sea surface temperature data

We propose to detect singular-features in order to generate an intelligent summary of high resolution spatio-temporal data that are obtained from satellite-based observations of the ocean. Toward this objective, we extend the Horn-Schunck model of flow field computation to incorporate incompressibility for tracking fluid motion. This is expressed as a zero-divergence constraint in the variational problem and an efficient multigrid implementation of it is introduced. Additionally, we show an effective localization of event features, such as vortices and saddle points, in the velocity field that can be used for subsequent abstraction, query and statistical analysis.
Shape from equal thickness contours

A unique imaging modality based on Equal Thickness Contours (ETC) has introduced a new opportunity for 3D shape reconstruction from multiple views. We present a computational framework for representing each view of an object in terms of its object thickness, and then integrating these representations into a 3D surface by algebraic reconstruction. The object thickness is inferred by grouping curve segments that correspond to points of second derivative maxima. At each step of the process, we use some form of regularization to ensure closeness to the original features, as well as neighborhood continuity. We apply our approach to images of a sub-micron crystal structure obtained through a holographic process.
Model based segmentation of nuclei

A new approach for segmentation of nuclei observed with an epi-fluorescence microscope is presented. The technique is model based and uses local feature activities such as step-edge segments, roof-edge segments, and concave corners to construct a set of initial hypotheses. These local feature activities are extracted using either local or global operators to form a possible set of hypotheses. Each hypothesis is expressed as a hyperquadric for better stability, compactness, and error handling. The search space is expressed as an assignment matrix with an appropriate cost function to ensure local adjacency, and global consistency. Each possible configuration of a set of nuclei defines a path, and the path with the least error corresponds to best representation. This result is then presented to an operator who verifies and eliminates a small number of errors.
Nuclei segmentation via sparsity constrained convolutional regression

Automated profiling of nuclear architecture, in histology sections, can potentially help predict the clinical outcomes. However, the task is challenging as a result of nuclear pleomorphism and cellular states (e.g, cell fate, cell cycle), which are compounded by the batch effect (e.g, variations in fixation and staining). Present methods, for nuclear segmentation, are based on human-designed features that may not effectively capture intrinsic nuclear architecture. In this paper, we propose a novel approach, called sparsity constrained convolutional regression (SCCR), for nuclei segmentation. Specifically, given raw image patches and the corresponding annotated binary masks, our algorithm jointly learns a bank of convolutional filters and a sparse linear regressor, where the former is used for feature extraction, and the latter aims to produce a likelihood for each pixel being nuclear region or background. During classification, the pixel label is simply determined by a thresholding operation applied on the likelihood map. The method has been evaluated using the benchmark dataset collected from The Cancer Genome Atlas (TCGA). Experimental results demonstrate that our method outperforms traditional nuclei segmentation algorithms and is able to achieve competitive performance compared to the state-of-the-art algorithm built upon human-designed features with biological prior knowledge.
Integrated profiling of cell surface protein and nuclear marker for discriminant analysis

Cell membrane proteins play an important role in tissue architecture and cell-cell communication. We hypothesize that segmentation and multivariate characterization of the distribution of cell membrane proteins, on a cell-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 (i) delineate cell membrane protein signals and associate them with specific nuclei, (ii) compute a coupled representation of the multiplexed DNA content with membrane proteins and other end points, (iii) evaluate computed features associated with such a multivariate representation, and (iv) discriminate between treatment groups in an optimal fashion. The novelty of our method is in the segmentation of the membrane signal and the multivariate representation of phenotypes on a cell-cell basis. To test the utility of the new 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 TGFbeta. These samples are labeled for their DNA content and E-cadherin membrane protein. We demonstrate that multivariate representation of cell-cell phenotypes improves predictive and visualization capabilities among different treatment groups, and increases quantitative sensitivity of cellular responses.
Predictive sparse morphometric context for classification of histology sections

Classification of histology sections from large cohorts, in terms of distinct regions of microanatomy (e.g., tumor, stroma, normal), enables the quantification of tumor composition, and the construction of predictive models of the clinical outcome. To tackle the batch effects and biological heterogeneities that are persistent in large cohorts, sparse cellular morphometric context has recently been developed for invariant representation of the underlying properties in the data, which summarizes cellular morphometric features at various locations and scales, and leads to a system with superior performance for classification of microanatomy and histopathology. However, the sparse optimization protocol for the calculation of sparse cellular morphometric features is not scalable for large scale classification. To improve the scalability of systems, based on sparse morphometric context, we propose the predictive sparse morphometric context in place of the original implementation, which approximates the sparse cellular morphometric feature through a non-linear regressor that is jointly learned with an over-complete dictionary in an unsupervised manner. Experimental results indicates over 50 times speedup compared to our previous implementation with the help of non-linear regressor; while producing competitive performance.
A Collaborative Framework for Distributed Microscopy

This paper outlines the motivation, requirements, and architecture of a collaborative framework for distributed virtual microscopy. In this context, the requirements are specified in terms of (1) functionality, (2) scalability, (3) interactivity, and (4) safety and security. Functionality refers to what and how an instrument does something. Scalability refers to the number of instruments, vendor specific desktop workstations, analysis programs, and collaborators that can be accessed. Interactivity refers to how well the system can be steered either for static or dynamic experiments. Safety and security refers to safe operation of an instrument coupled with user authentication, privacy, and integrity of data communication. To meet these requirements, we introduce three types of services in the architecture: Instrument Services (IS), Exchange Services (ES), and Computational Services (CS). These services may reside on any host in the distributed system. The IS provide an abstraction for manipulating different types of microscopes; the ES provide common services that are required between different resources; and the CS provide analytical capabilities for data analysis and simulation. These services are brought together through CORBA and its enabling services, e.g., Event Services, Time Services, Naming Services, and Security Services. Two unique applications have been introduced into the CS for analyzing scientific images either for instrument control or recovery of a model for objects of interest. These include: insitu electron microscopy and recovery of 3D shape from holographic microscopy. The first application provides a near real-time processing of the video-stream for on-line quantitative analysis and the use of that information for closed-loop servo control. The second application reconstructs a 3D representation of an inclusion (a crystal structure in a matrix) from multiple views through holographic electron microscopy. These application require steering external stimuli or computational parameters for a particular result. In a sense, ``computational instruments'' (symmetric multiprocessors) interact closely with data generated from ``experimental instruments'' (unique microscopes) to conduct new experiments and bring new functionalities to these instruments. Both of these features exploit high-performance computing and low-latency networks to bring novel functionalities to unique scientific imaging instruments.
Detection of 3D filamentous networks from tomographic electron microscopy

This paper presents a computational approach for detection of filamentous networks in 3D tomographic electron microscopy. Due to the general heterogeneity of chemical staining, imaged signatures may appear punctate and discontinuous. Very often, there is a necessity to characterize organization and phenotypic signatures of stained structures. This is the example of polysaccharides in the plant cell wall, where characterization of its inner structure allows for studies of their mechanical properties and accessibility. Our approach consists of three steps: (i) Hessian filtering for signal enhancement; (ii) tensor voting for detection and completion of organized patterns; and (iii) curve tracking for segmentation of filaments and junctions. We validated our approach by detecting filaments in synthetic images and real plant cell walls imaged by 3D tomographic electron microscopy.
Visual servoing for micro-manipulation

A framework for automated micro-manipulation of microscopic objects using visual information is developed and applied to diverse problems in biology and material science. These applications include microdissection of stretched and immobilized DNA molecules using an optical microscope and dynamic studies of crystal structure formation observed with a high power transmission electron microscope. Manipulation in this context refers not only to mechanical positioning or dissection of a specimen, but also to the variation of environmental parameters that elicit a response in the specimen. The common thread between these applications is a collection of computational techniques for image compression, autofocusing, object detection, tracking, and servo loop control. A second feature of our system is a distributed software architecture that allows the proposed dynamic experiments to be conducted remotely over the wide area network. In this context, the visual information processing is used to compensate for dynamic changes and the latencies in the wide area network. This system is now available over the global Internet.
Classification of Tumor Histology via Morphometric Context

Image-based classification of tissue histology, in terms of different components (e.g., normal signature, categories of aberrant signatures), provides a series of indices for tumor composition. Subsequently, aggregation of these indices in each whole slide image (WSI) from a large cohort can provide predictive models of clinical outcome. However, the performance of the existing techniques is hindered as a result of large technical and biological variations that are always present in a large cohort. In this paper, we propose two algorithms for classification of tissue histology based on robust representations of morphometric context, which are built upon nuclear level morphometric features at various locations and scales within the spatial pyramid matching (SPM) framework. These methods have been evaluated on two distinct datasets of different tumor types collected from The Cancer Genome Atlas (TCGA), and the experimental results indicate that our methods are (i) extensible to different tumor types, (ii) robust in the presence of wide technical and biological variations, (iii) invariant to different nuclear segmentation strategies, and (iv) scalable with varying training sample size. In addition, our experiments suggest that enforcing sparsity, during the construction of morphometric context, further improves the performance of the system.
A new regularized approach for contour morphing

In this paper, we propose a new approach for interpolating curves (contour morphing) in time, which is a process of gradually changing a source curve (known) through intermediate curves (unknown) into a target curve (known). The novelty of our approach is in the deployment of a new regularization term and the corresponding Euler equation. Our method is applicable to implicit curve representation and it establishes a relationship between curve interpolation and a two dimensional function. This is achieved by minimizing the supremum of the gradient, which leads to the infinite Laplacian equation (ILE). ILE is optimal in the sense that interpolated curves are equally distributed along their normal direction. We point out that the existing distance field manipulation (DFM) methods are only an approximation to the proposed optimal solution and that the relationship between ILE and DFM is not local as it has been asserted before. The proposed interpolation can also be used to construct multiscale curve representation.
Structural annotation of em images by graph cut

Biological images have the potential to reveal complex signatures that may not be amenable to morphological modeling in terms of shape, location, texture, and color. An effective analytical method is to characterize the composition of a specimen based on user-defined patterns of texture and contrast formation. However, such a simple requirement demands an improved model for stability and robustness. Here, an interactive computational model is introduced for learning patterns of interest by example. The learned patterns bound an active contour model in which the traditional gradient descent optimization is replaced by the more efficient optimization of the graph cut methods. First, the energy function is defined according to the curve evolution. Next, a graph is constructed with weighted edges on the energy function and is optimized with the graph cut algorithm. As a result, the method combines the advantages of the level set method and graph cut algorithm, i.e., “topological” invariance and computational efficiency. The technique is extended to the multiphase segmentation problem; the method is validated on synthetic images and then applied to specimens imaged by transmission electron microscopy(TEM).