Cell-based quantification of molecular biomarkers in histopathology specimens.

AIMS: To investigate the use of a computer-assisted technology for objective, cell-based quantification of molecular biomarkers in specified cell types in histopathology specimens, with the aim of advancing current visual estimation and pixel-level (rather than cell-based) quantification methods. METHODS AND RESULTS: Tissue specimens were multiplex-immunostained to reveal cell structures, cell type markers, and analytes, and imaged with multispectral microscopy. The image data were processed with novel software that automatically delineates and types each cell in the field, measures morphological features, and quantifies analytes in different subcellular compartments of specified cells.The methodology was validated with the use of cell blocks composed of differentially labelled cultured cells mixed in known proportions, and evaluated on human breast carcinoma specimens for quantifying human epidermal growth factor receptor 2, estrogen receptor, progesterone receptor, Ki67, phospho-extracellular signal-related kinase, and phospho-S6. Automated cell-level analyses closely matched human assessments, but, predictably, differed from pixel-level analyses of the same images. CONCLUSIONS: Our method reveals the type, distribution, morphology and biomarker state of each cell in the field, and allows multiple biomarkers to be quantified over specified cell types, regardless of their abundance. It is ideal for studying specimens from patients in clinical trials of targeted therapeutic agents, for investigating minority stromal cell subpopulations, and for phenotypic characterization to personalize therapy and prognosis.

Fast Graph Partitioning Active Contours for Image Segmentation Using Histograms.

We present a method to improve the accuracy and speed, as well as significantly reduce the memory requirements, for the recently proposed Graph Partitioning Active Contours (GPACs) algorithm for image segmentation in the work of Sumengen and Manjunath (2006). Instead of computing an approximate but still expensive dissimilarity matrix of quadratic size, ( N s 2 M s 2 ) ∕ ( n s m s ) , for a 2D image of size Ns ×Ms and regular image tiles of size ns ×ms , we use fixed length histograms and an intensity-based symmetric-centrosymmetric extensor matrix to jointly compute terms associated with the complete NsMs × NsMs dissimilarity matrix. This computationally efficient reformulation of GPAC using a very small memory footprint offers two distinct advantages over the original implementation. It speeds up convergence of the evolving active contour and seamlessly extends performance of GPAC to multidimensional images.

Tracking cells in Life Cell Imaging videos using topological alignments.

BACKGROUND: With the increasing availability of live cell imaging technology, tracking cells and other moving objects in live cell videos has become a major challenge for bioimage informatics. An inherent problem for most cell tracking algorithms is over- or under-segmentation of cells - many algorithms tend to recognize one cell as several cells or vice versa. RESULTS: We propose to approach this problem through so-called topological alignments, which we apply to address the problem of linking segmentations of two consecutive frames in the video sequence. Starting from the output of a conventional segmentation procedure, we align pairs of consecutive frames through assigning sets of segments in one frame to sets of segments in the next frame. We achieve this through finding maximum weighted solutions to a generalized "bipartite matching" between two hierarchies of segments, where we derive weights from relative overlap scores of convex hulls of sets of segments. For solving the matching task, we rely on an integer linear program. CONCLUSION: Practical experiments demonstrate that the matching task can be solved efficiently in practice, and that our method is both effective and useful for tracking cells in data sets derived from a so-called Large Scale Digital Cell Analysis System (LSDCAS). AVAILABILITY: The source code of the implementation is available for download from http://www.picb.ac.cn/patterns/Software/topaln.

Flux Tensor Constrained Geodesic Active Contours with Sensor Fusion for Persistent Object Tracking.

This paper makes new contributions in motion detection, object segmentation and trajectory estimation to create a successful object tracking system. A new efficient motion detection algorithm referred to as the flux tensor is used to detect moving objects in infrared video without requiring background modeling or contour extraction. The flux tensor-based motion detector when applied to infrared video is more accurate than thresholding "hot-spots", and is insensitive to shadows as well as illumination changes in the visible channel. In real world monitoring tasks fusing scene information from multiple sensors and sources is a useful core mechanism to deal with complex scenes, lighting conditions and environmental variables. The object segmentation algorithm uses level set-based geodesic active contour evolution that incorporates the fusion of visible color and infrared edge informations in a novel manner. Touching or overlapping objects are further refined during the segmentation process using an appropriate shape-based model. Multiple object tracking using correspondence graphs is extended to handle groups of objects and occlusion events by Kalman filter-based cluster trajectory analysis and watershed segmentation. The proposed object tracking algorithm was successfully tested on several difficult outdoor multispectral videos from stationary sensors and is not confounded by shadows or illumination variations.

Cell segmentation using coupled level sets and graph-vertex coloring.

Current level-set based approaches for segmenting a large number of objects are computationally expensive since they require a unique level set per object (the N-level set paradigm), or [log2N] level sets when using a multiphase interface tracking formulation. Incorporating energy-based coupling constraints to control the topological interactions between level sets further increases the computational cost to O(N2). We propose a new approach, with dramatic computational savings, that requires only four, or fewer, level sets for an arbitrary number of similar objects (like cells) using the Delaunay graph to capture spatial relationships. Even more significantly, the coupling constraints (energy-based and topological) are incorporated using just constant O(1) complexity. The explicit topological coupling constraint, based on predicting contour collisions between adjacent level sets, is developed to further prevent false merging or absorption of neighboring cells, and also reduce fragmentation during level set evolution. The proposed four-color level set algorithm is used to efficiently and accurately segment hundreds of individual epithelial cells within a moving monolayer sheet from time-lapse images of in vitro wound healing without any false merging of cells.

QUANTITATIVE CELL MOTILITY FOR IN VITRO WOUND HEALING USING LEVEL SET-BASED ACTIVE CONTOUR TRACKING.

Quantifying the behavior of cells individually, and in clusters as part of a population, under a range of experimental conditions, is a challenging computational task with many biological applications. We propose a versatile algorithm for segmentation and tracking of multiple motile epithelial cells during wound healing using time-lapse video. The segmentation part of the proposed method relies on a level set-based active contour algorithm that robustly handles a large number of cells. The tracking part relies on a detection-based multiple-object tracking method with delayed decision enabled by multi-hypothesis testing. The combined method is robust to complex cell behavior including division and apoptosis, and to imaging artifacts such as illumination changes.