BACKGROUND: Our goals are to develop a computational histopathology pipeline for characterizing tumor types that are being generated by The Cancer Genome Atlas (TCGA) for genomic association. TCGA is a national collaborative program where different tumor types are being collected, and each tumor is being characterized using a variety of genome-wide platforms. Here, we have developed a tumor-centric analytical pipeline to process tissue sections stained with hematoxylin and eosin (H&E) for visualization and cell-by-cell quantitative analysis. Thus far, analysis is limited to Glioblastoma Multiforme (GBM) and kidney renal clear cell carcinoma tissue sections. The final results are being distributed for subtyping and linking the histology sections to the genomic data. RESULTS: A computational pipeline has been designed to continuously update a local image database, with limited clinical information, from an NIH repository. Each image is partitioned into blocks, where each cell in the block is characterized through a multidimensional representation (e.g., nuclear size, cellularity). A subset of morphometric indices, representing potential underlying biological processes, can then be selected for subtyping and genomic association. Simultaneously, these subtypes can also be predictive of the outcome as a result of clinical treatments. Using the cellularity index and nuclear size, the computational pipeline has revealed five subtypes, and one subtype, corresponding to the extreme high cellularity, has shown to be a predictor of survival as a result of a more aggressive therapeutic regime. Further association of this subtype with the corresponding gene expression data has identified enrichment of (i) the immune response and AP-1 signaling pathways, and (ii) IFNG, TGFB1, PKC, Cytokine, and MAPK14 hubs. CONCLUSION: While subtyping is often performed with genome-wide molecular data, we have shown that it can also be applied to categorizing histology sections. Accordingly, we have identified a subtype that is a predictor of the outcome as a result of a therapeutic regime. Computed representation has become publicly available through our Web site.

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.

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.

We propose an approach for surface recovery from planar sectional contours. The surface is reconstructed based on the so-called "equal importance criterion" which suggests that every point in the region contributes equally to the reconstruction process. The problem is then formulated in terms of a partial differential equation, and the solution is efficiently calculated from a distance transformation. To make the algorithm valid for different application purposes, both the isosurface and the primitive representations of the object surface are derived. The isosurface is constructed by a partial differential equation, which can be solved iteratively. The traditional distance interpolating method, which was used by several researchers for surface reconstruction, is an approximate solution of the PDE. The primitive representations are approximated by Voronoi diagram transformation of the surface space. Isosurfaces have the advantage that subsequent geometric analysis of the object can be easily carried out while primitive representation is easy to visualize. The proposed technique allows for surface recovery at any desired resolution, thus avoiding the inherent problems of correspondence, tiling, and branching.

A unique imaging modality based on equal thickness contours (ETC) has introduced a new opportunity for 3D shape reconstruction from multiple views. These ETC can be generated through an interference between transmitted and diffracted beams. 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. In this framework, the object thickness is first derived from ideal contours and then extended to real data. For real data, 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 submicron crystal structure obtained through a holographic process.

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.

Article DeepView: a channel for distributed microscopy and informatics Share on Authors: B. Parvin Information and Computing Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA Information and Computing Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CAView Profile , J. Taylor Information and Computing Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA Information and Computing Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CAView Profile , G. Cong Information and Computing Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA Information and Computing Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CAView Profile , M. A. OKeefe Material Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA Material Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CAView Profile , M. H. Barcellos-Hoff Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CAView Profile Authors Info & Claims SC '99: Proceedings of the 1999 ACM/IEEE conference on SupercomputingJanuary 1999 Pages 65–eshttps://doi.org/10.1145/331532.331597Online:01 January 1999Publication History 6citation214DownloadsMetricsTotal Citations6Total Downloads214Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

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.

A unique imaging modality based on equal thickness contours (ETC) has introduced a new opportunity for 3D shape reconstruction from multiple views. These ETCs are generated as a result of interference between transmitted and diffracted beams. 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. In this framework, the object thickness is first derived from ideal contours and then extended to real data. For real data, 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 graphic process.

A curve evolution approach is proposed for corner enhancement. The constraint is that a new corner should not be generated. Starting from the general geometric heat flow (GGHF), we study under what conditions the GGHF satisfies the scale space causality criteria. The criteria of corner enhancement are also proposed. Then, a new curve evolution scheme which can enhance strong corners, suppress noise and satisfies the scale space criteria is presented.

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.

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.

We approximate Gaussian function with any scale by linear combination of Gaussian functions with dyadic scales so that scale space can be constructed much more efficiently. The approximation error is so small that our approach can be used widely in computer vision and pattern recognition. Features at any scale can also be found efficiently by tracking from the dyadic scales.

Thin network extraction from three dimensional images is a new issue in computer vision. It is of major importance in medical vascular imaging for diagnostic, therapy planning and surgery. In this paper, we develop a framework for automatic thin network extraction from the volumic image. The approach consists in treating the 3D image as a hyper-surface of IR/sup 4/. It is shown that the crest points of this hyper-surface correspond to the center line of the thin network in the image. Using a simple mathematical model, we establish the relationship between the computed principal curvatures of the hyper-surface and the geometry of the network. Promising results are shown on synthetic and real vascular images.