nils-herrmann/clean_scirepeval huggingface dataset:
Tracking of Tubular Molecules for Scientific Applications

In this paper, we present a system for detection and tracking of tubular molecules in images. The automatic detection and characterization of the shape, location, and motion of these molecules can enable new laboratory protocols in several scientific disciplines. The uniqueness of the proposed system is twofold: At the macro level, the novelty of the system lies in the integration of object localization and tracking using geometric properties; at the micro level, in the use of high and low level constraints to model the detection and tracking subsystem. The underlying philosophy for object detection is to extract perceptually significant features from the pixel level image, and then use these high level cues to refine the precise boundaries. In the case of tubular molecules, the perceptually significant features are antiparallel line segments or, equivalently, their axis of symmetries. The axis of symmetry infers a coarse description of the object in terms of a bounding polygon. The polygon then provides the necessary boundary condition for the refinement process, which is based on dynamic programming. For tracking the object in a time sequence of images, the refined contour is then projected onto each consecutive frame.
Scoring histological sections through immunohistochemistry

Immunohistochemical staining of biological samples provides a rapid protocol for visualizing various complexes and organelles. However, this method of sample staining is seldom used for direct quantitative analysis due to variations in sample fixations, ambiguities introduced by color composition, and the limited dynamic range of imaging instruments. We demonstrate that, through the decomposition of color signals, staining can be scored on a cell-by-cell basis. We applied our method to fibroblasts grown from histologically normal breast tissue biopsies obtained from two populations. Nuclear regions are initially segmented by a cascade of filters and geometric constraints. Subsequently, the strength of staining is quantified by a color decomposition model that is optimized by graph cut algorithm. By including color decomposition in the process, nuclear segmentation can be validated (corrected) for subsequent geometric representation. Finally, signal complexes are associated with each nuclear region following region-based Voronoi tessellation.
QUANTITATIVE REPRESENTATION OF THREE-DIMENSIONAL CELL CULTURE MODELS

Three-dimensional mammary cell culture models offer new opportunities for the development of computational techniques for segmentation, localization, and multicellular organization. Under normal conditions, these assays form a symmetrical, hollow structure, which is necessary for their functional operation. Often, the nuclear compartments are labeled, which provides context for quantitative protein localization or colony structure through fluorescent microscopy. These colonies are first delineated from the background using the level set method. Within each colony, nuclear regions are then bounded by their center of mass through iterative radial voting, and a local neighborhood for each nucleus is established through Voronoi tessellation. Finally, the level set method is applied again within each Voronoi region to delineate the nuclear compartment. The paper concludes with the application of the proposed method to a set of experimental data demonstrating a stable solution when iterative radial voting and level set methods are used synergistically. Furthermore, segmented colonies are characterized for architectural changes as a result of ionizing radiation
Multireference Level Set for the Characterization of Nuclear Morphology in Glioblastoma Multiforme

Histological tissue sections provide rich information and continue to be the gold standard for the assessment of tissue neoplasm. However, there are a significant amount of technical and biological variations that impede analysis of large histological datasets. In this paper, we have proposed a novel approach for nuclear segmentation in tumor histology sections, which addresses the problem of technical and biological variations by incorporating information from both manually annotated reference patches and the original image. Subsequently, the solution is formulated within a multireference level set framework. This approach has been validated on manually annotated samples and then applied to the TCGA glioblastoma multiforme (GBM) dataset consisting of 440 whole mount tissue sections scanned with either a 20× or 40 × objective, in which, each tissue section varies in size from 40k × 40k pixels to 100k × 100k pixels. Experimental results show a superior performance of the proposed method in comparison with present state of art techniques.
BioSig: an imaging bioinformatics system for phenotypic analysis

Organisms express their genomes in a cell-specific manner, resulting in a variety of cellular phenotypes or phenomes. Mapping cell phenomes under a variety of experimental conditions is necessary in order to understand the responses of organisms to stimuli. Representing such data requires an integrated view of experimental and informatic protocols. The proposed system, named BioSig, provides the foundation for cataloging cellular responses as a function of specific conditioning, treatment, staining, etc., for either fixed tissue or living cell studies. A data model has been developed to capture experimental variables and map them to image collections and their computed representation. This representation is hierarchical and spans across sample tissues, cells, and organelles, which are imaged with light microscopy. At each layer, content is represented with an attributed graph, which contains information about cellular morphology, protein localization, and cellular organization in tissue or cell culture. The Web-based multilayer informatics architecture uses the data model to provide guided workflow access for content exploration.
Batch-invariant nuclear segmentation in whole mount histology sections

The Cancer Genome Atlas (TCGA) provides a rich repository of whole mount tumor sections that are collected from different laboratories. However, there are a significant amount of technical and biological variations that impede analysis. We have developed a novel approach for nuclear segmentation in histology sections, which addresses the problem of technical and biological variations by incorporating information from manually annotated reference patches with the local color space of the original image. Subsequently, the problem is formulated within a multi-reference graph cut with geodesic constraints. This approach has been validated on manually curated samples and then applied to a dataset of 440 whole mount tissue sections, originating from different laboratories, which are typically 40k-by-40k pixels or larger. Segmentation results, through a zoomable interface, and extracted morphometric data are available at: http://tcga.lbl.gov.
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 small 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.
Invariant representation and hierarchical network for inspection of nuts from X-ray images

An X-ray based system for the inspection of pistachio nuts and wheat kernels for internal insect infestation is presented. The novelty of this system is two-fold. First, we construct an invariant representation of infested nuts from X-ray images that is rich, robust, and compact. Insect infestation creates a tunnel, in the X-ray image, with reduced density of the natural material. The tunneling effect is encoded by linking troughs on the image and constructing a joint curvature-proximity distribution table for each nut. The latter step is designed to accentuate separation of those tunneling effects that are due to the natural structure of the nut. Second, since the representation is sparse, we partition the joint distribution table into several regions, where each region is used independently to train a backpropagation (BP) network. The outputs of these subnets are then collectively trained with another BP network. We show that the resulting hierarchical network has the advantage of reduced dimensionality while maintaining a performance similar to the standard BP network.
Quantum cascade laser infrared microscopy differentiates malignant phenotypes in breast histology sections

Quantum Cascade Laser InfRared (QCL-IR) microscopy has the potential to emerge as a unique modality for the diagnostics of histology sections. A pilot experiment is designed to evaluate whether benign and malignant breast histology sections can be differentiated using chemical profiling and without the use of spatial information. The experiment consists of 22 independent samples that were randomly selected from paraffin-embedded blocks with an equal number of benign and malignant labels. Spectral data are normalized and then used for both visualization and classification. Visualization is based on consensus clustering of the spectral signature within each group of the benign or malignant phenotype. Classification has been evaluated with three methods of tree-based, convolutional neural networks, and an encoder module for compression followed by softmax classification. The latter has the best performance with using only 20% of the data for training to arrive at the classification accuracy of 100%. Direct analysis of the spectral signatures indicates that both malignant and benign samples express the similar spectral peaks; however, peaks corresponding to several nucleic acid sequences and protein groups are overexpressed in malignant tissues.
Spectral Decomposition of Signaling Networks

Many dynamical processes can be represented as directed attributed graphs or Petri nets where relationships between various entities are explicitly expressed. Signaling networks modeled as Petri nets are one class of such graphical modeling and representations. These networks encode how different protein in specific compartments, interact to create new protein products. Initially, the proteins and rules governing their interactions are curated from literature and then refined with experimental data. Variation in these networks occurs in topological structure, size, and weights associated on edges. Collectively, these variations are quite significant for manual and interactive analysis. Furthermore, as new information is added to these networks, the emergence of new computational models becomes paramount. From this perspective, hierarchical spectral methods are proposed and applied for inferring similarities and dissimilarities from an ensemble of graphs that corresponds to reaction networks. The technique has been implemented and tested on curated signaling networks that are derived for breast cancer cell lines
Surface reconstruction from sparse fringe contours

A new approach for reconstruction of 3D surfaces from 2D cross-sectional contours is presented. By using the so-called "Equal Importance Criterion," we reconstruct the surface based on the assumption that every point in the region contributes equally to the surface reconstruction process. In this context, the problem is formulated in terms of a partial differential equation (PDE), and we show that the solution for dense contours can be efficiently derived from distance transform. In the case of sparse contours, we add a regularization term to insure smoothness in surface recovery. The proposed technique allows for surface recovery at any desired resolution. The main advantage of the proposed method is that inherent problems due to correspondence, tiling, and branching are avoided. Furthermore, the computed high resolution surface is better represented for subsequent geometric analysis. We present results on both synthetic and real data.
SEGMENTATION OF MAMMOSPHERE STRUCTURES FROM VOLUMETRIC DATA

3D cell culture assays have emerged as the basis of an improved model system for evaluating therapeutic agents, molecular probes, and exogenous stimuli. However, there is a gap in robust computational techniques for segmentation of image data that are collected through confocal or deconvolution microscopy. The main issue is the volume of data, overlapping subcellular compartments, and variation in scale and size of subcompartments of interest. A geometric technique has been developed to bound the solution of the problem by first localizing centers of mass for each cell and then partitioning clumps of cells along minimal intersecting surfaces. An approximate solution to the center of mass is realized through iterative spatial voting, which is tolerant to variation in shape morphologies and overlapping compartments and is shown to have an excellent noise immunity. These approximate estimates to centers of mass are then used to partition a clump of cells along minimal intersecting surfaces that are estimated by Radon transform. Examples on real data and performance of the system over a large population of data are evaluated. Furthermore, it is shown that the proposed methodology is extensible in terms of its application to protein localization studies
Tunable tensor voting for regularizing punctate patterns of membrane-bound protein signals

Membrane-bound protein, expressed in the basal-lateral region, is heterogeneous and an important endpoint for understanding biological processes. At the optical resolution, membrane-bound protein can be visualized as being diffused (e.g., E-cadherin), punctate (e.g., connexin), or simultaneously diffused and punctate as a result of sample preparation or conditioning. Furthermore, there is a signi..cant amount of heterogeneity as a result of technical and biological variations. This paper aims at enhancing membrane-bound proteins that are expressed between epithelial cells so that quantitative analysis can be enabled on a cell-by-cell basis. We propose a method to detect and enhance membrane-bound protein signal from noisy images. More precisely, we build upon the tensor voting framework in order to produce an ef..cient method to detect and re..ne perceptually interesting linear structures in images. The novelty of the proposed method is in its iterative tuning of the tensor voting fields, which allows the concentration of the votes only over areas of interest. The method is shown to produce high quality enhancements of membrane-bound protein signals with combined punctate and diffused characteristics. Experimental results demonstrate the benefits of using tunable tensor voting for enhancing and differentiating cell-cell adhesion mediated by integral cell membrane protein.
BioSig: An Imaging Bioinformatic System for Studying Phenomic

Using genomic information to understand complex organisms requires comprehensive knowledge of the dynamics of phenotype generation and maintenance. A phenotype results from selective expression of the genome, creating a history of the cell and its response to the extracellular environment. Defining cell phenomes requires tracking the kinetics and quantities of multiple constituent proteins, their cellular context, and their morphological features in large populations. The paper considers how the BioSig imaging bioinformatic system for characterizing phenomics answers these challenges. The BioSig approach to microscopy and quantitative image analysis helps to build a more detailed picture of the signaling that occurs between cells as a response to exogenous stimulus such as radiation or as a consequence of endogenous programs leading to biological functions. The system provides a data model for capturing experimental annotations and variables, computational techniques for summarizing large numbers of images, and a distributed architecture that facilitates distant collaboration.
Chemical profiling of the plant cellwall through Raman microspectroscopy

This paper presents a computational framework for chemical profiling of the plant cell wall through the Raman spectroscopy. The system enables query of known spectral signatures and clustering of spectral data based on intrinsic properties. As a result, presence and relative concentration of specific chemical bonds can be quantified. The primary contribution of this paper is in representation of raman profile in terms of fluorescence background and multiscale peak detection at each grid point (voxel). Such a representation allows efficient spatial segmentation based on the coupling between high-level salient properties and low-level symbolic representation at each voxel. The high-level salient properties refer to preferred peaks and their attributes for the entire image. The low-level symbolic representations are based on fluorescence background, spectral peak locations, and their attributes. We present results on a corn stover tissue section that is imaged through Raman microscopy, and the results are consistent with the literature. In addition, automatic clustering indicates several distinct layers of the cell walls with different spectral signatures.
Feature-Based Representation Improves Color Decomposition and Nuclear Detection Using a Convolutional Neural Network

Detection of nuclei is an important step in phenotypic profiling of 1) histology sections imaged in bright field; and 2) colony formation of the 3-D cell culture models that are imaged using confocal microscopy. It is shown that feature-based representation of the original image improves color decomposition (CD) and subsequent nuclear detection using convolutional neural networks independent of the imaging modality. The feature-based representation utilizes the Laplacian of Gaussian (LoG) filter, which accentuates blob-shape objects. Moreover, in the case of samples imaged in bright field, the LoG response also provides the necessary initial statistics for CD using nonnegative matrix factorization. Several permutations of input data representations and network architectures are evaluated to show that by coupling improved CD and the LoG response of this representation, detection of nuclei is advanced. In particular, the frequencies of detection of nuclei with the vesicular or necrotic phenotypes, or poor staining, are improved. The overall system has been evaluated against manually annotated images, and the F-scores for alternative representations and architectures are reported.
Tracking of tubular objects for scientific applications

We present a system for detection, tracking and representation of tubular objects in images. The uniqueness of the proposed system is twofold: at the macro level, the novelty of the system lies in the integration of object localization and tracking using geometric properties; at the micro level, is the use of high and low level constraints to model the detection and tracking subsystem. The underlying philosophy for object detection is to extract perceptually significant features from the pixel level image, and then use these high level cues to refine the precise boundaries. In the case of tubular objects, the perceptually significant features are anti-parallel line segments or, equivalently, their axis of symmetries. The axis of symmetry infers a coarse description of the object in terms of a bounding polygon. The polygon then provides the necessary boundary condition for the refinement process, which is based on dynamic programming. For tracking the object in a time sequence of images, the refined contour is then projected onto each consecutive frame. In addition, the system provides an axis of symmetry representation of object for subsequent scientific analysis.<<ETX>>
Visual Servoing for Online Facilities

Visual servoing for online facilities aims at automated manipulation of instrument control parameters based on visual-scene interpretation. It provides a layer of computing that hides the latency in the Internet environment and simplifies the use of scientific imaging instruments. This approach makes remote real-time imaging experiments feasible over unpredictable wide area networks. The authors' efforts focus on developing a framework for remotely manipulating microscopic objects. Two diverse applications demonstrate the framework's usefulness: microdissection of DNA molecules and in situ examination of crystal formation. Microdissection and subsequent amplification of DNA molecules allow for rapid closure of gaps in the genomic library. In situ experiments reveal information about the thermal and morphological properties of crystal structures.
Automated Histology Analysis: Opportunities for signal processing

Histology is the microscopic inspection of plant or animal tissue. It is a critical component in diagnostic medicine and a tool for studying the pathogenesis and biology of processes such as cancer and embryogenesis. Tissue processing for histology has become increasingly automated, drastically increasing the speed at which histology labs can produce tissue slides for viewing. Another trend is the digitization of these slides, allowing them to be viewed on a computer rather than through a microscope. Despite these changes, much of the routine analysis of tissue sections remains a painstaking, manual task that can only be completed by highly trained pathologists at a high cost per hour. There is, therefore, a niche for image analysis methods that can automate some aspects of this analysis. These methods could also automate tasks that are prohibitively time-consuming for humans, e.g., discovering new disease markers from hundreds of whole-slide images (WSIs) or precisely quantifying tissues within a tumor.
A dynamic system for object description and correspondence

A unified framework for object description and correspondence in range images is proposed. This is based on modeling the ambiguities with a dynamic system at every level of hierarchy. The critical issue in object description is boundary completion. The authors show how this step can be modeled as the interaction of long and short term variables operating on partial contours, represented by parametric splines. In a sense, this network operates on an evolutionary basis where boundary completion is modeled as a synergistic process, and mutation is modeled as differential decay. Once reliable surface features are extracted, the same framework, with appropriate constraints, can find the corresponding surfaces and compute the transformation.<<ETX>>