A computational model of chemotaxis-based cell aggregation.

We present a computational model that successfully captures the cell behaviors that play important roles in 2-D cell aggregation. A virtual cell in our model is designed as an independent, discrete unit with a set of parameters and actions. Each cell is defined by its location, size, rates of chemoattractant emission and response, age, life cycle stage, proliferation rate and number of attached cells. All cells are capable of emitting and sensing a chemoattractant chemical, moving, attaching to other cells, dividing, aging and dying. We validated and fine-tuned our in silico model by comparing simulated 24-h aggregation experiments with data derived from in vitro experiments using PC12 pheochromocytoma cells. Quantitative comparisons of the aggregate size distributions from the two experiments are produced using the Earth Mover's Distance (EMD) metric. We compared the two size distributions produced after 24 h of in vitro cell aggregation and the corresponding computer simulated process. Iteratively modifying the model's parameter values and measuring the difference between the in silico and in vitro results allow us to determine the optimal values that produce simulated aggregation outcomes closely matched to the PC12 experiments. Simulation results demonstrate the ability of the model to recreate large-scale aggregation behaviors seen in live cell experiments.

Estimation of Fine-Scale Histologic Features at Low Magnification.

CONTEXT.­: Whole-slide imaging has ushered in a new era of technology that has fostered the use of computational image analysis for diagnostic support and has begun to transfer the act of analyzing a slide to computer monitors. Due to the overwhelming amount of detail available in whole-slide images, analytic procedures-whether computational or visual-often operate at magnifications lower than the magnification at which the image was acquired. As a result, a corresponding reduction in image resolution occurs. It is unclear how much information is lost when magnification is reduced, and whether the rich color attributes of histologic slides can aid in reconstructing some of that information. OBJECTIVE.­: To examine the correspondence between the color and spatial properties of whole-slide images to elucidate the impact of resolution reduction on the histologic attributes of the slide. DESIGN.­: We simulated image resolution reduction and modeled its effect on classification of the underlying histologic structure. By harnessing measured histologic features and the intrinsic spatial relationships between histologic structures, we developed a predictive model to estimate the histologic composition of tissue in a manner that exceeds the resolution of the image. RESULTS.­: Reduction in resolution resulted in a significant loss of the ability to accurately characterize histologic components at magnifications less than ×10. By utilizing pixel color, this ability was improved at all magnifications. CONCLUSIONS.­: Multiscale analysis of histologic images requires an adequate understanding of the limitations imposed by image resolution. Our findings suggest that some of these limitations may be overcome with computational modeling.

An alternative reference space for H&E color normalization.

Digital imaging of H&E stained slides has enabled the application of image processing to support pathology workflows. Potential applications include computer-aided diagnostics, advanced quantification tools, and innovative visualization platforms. However, the intrinsic variability of biological tissue and the vast differences in tissue preparation protocols often lead to significant image variability that can hamper the effectiveness of these computational tools. We developed an alternative representation for H&E images that operates within a space that is more amenable to many of these image processing tools. The algorithm to derive this representation operates by exploiting the correlation between color and the spatial properties of the biological structures present in most H&E images. In this way, images are transformed into a structure-centric space in which images are segregated into tissue structure channels. We demonstrate that this framework can be extended to achieve color normalization, effectively reducing inter-slide variability.

An optimized color transformation for the analysis of digital images of hematoxylin & eosin stained slides.

Hematoxylin and eosin (H&E) staining is ubiquitous in pathology practice and research. As digital pathology has evolved, the reliance of quantitative methods that make use of H&E images has similarly expanded. For example, cell counting and nuclear morphometry rely on the accurate demarcation of nuclei from other structures and each other. One of the major obstacles to quantitative analysis of H&E images is the high degree of variability observed between different samples and different laboratories. In an effort to characterize this variability, as well as to provide a substrate that can potentially mitigate this factor in quantitative image analysis, we developed a technique to project H&E images into an optimized space more appropriate for many image analysis procedures. We used a decision tree-based support vector machine learning algorithm to classify 44 H&E stained whole slide images of resected breast tumors according to the histological structures that are present. This procedure takes an H&E image as an input and produces a classification map of the image that predicts the likelihood of a pixel belonging to any one of a set of user-defined structures (e.g., cytoplasm, stroma). By reducing these maps into their constituent pixels in color space, an optimal reference vector is obtained for each structure, which identifies the color attributes that maximally distinguish one structure from other elements in the image. We show that tissue structures can be identified using this semi-automated technique. By comparing structure centroids across different images, we obtained a quantitative depiction of H&E variability for each structure. This measurement can potentially be utilized in the laboratory to help calibrate daily staining or identify troublesome slides. Moreover, by aligning reference vectors derived from this technique, images can be transformed in a way that standardizes their color properties and makes them more amenable to image processing.

Lymph Node Metastasis Status in Breast Carcinoma Can Be Predicted via Image Analysis of Tumor Histology.

OBJECTIVE: To develop a method whereby axillary lymph node (ALN) metastasis can be predicted without ALN dissection, via computational image analysis of routinely acquired tumor histology. STUDY DESIGN: We employed digital image processing to stratify patients based on the histological attributes of the primary tumor. We extracted image features that capture the nuclear and architectural properties of the specimen. We then used a novel machine learning algorithm to transform image features into a scalar score that provided not only a metastasis prediction but also the certainty of classification. RESULTS: We applied this procedure to 101 patients with a ground truth established by histological examination of the lymph nodes and found that 68.3% of the cohort could be classified, exhibiting a correct prediction rate of 88.4%. CONCLUSION: These results demonstrate a technique that potentially can be used to supplant existing surgical methods to determine ALN metastasis status, thereby reducing patient morbidity associated with over-treatment.

Automated analysis of courtship suppression learning and memory in Drosophila melanogaster.

Study of the fruit fly, Drosophila melanogaster, has yielded important insights into the underlying molecular mechanisms of learning and memory. Courtship conditioning is a well-established behavioral assay used to study Drosophila learning and memory. Here, we describe the development of software to analyze courtship suppression assay data that correctly identifies normal or abnormal learning and memory traits of individual flies. Development of this automated analysis software will significantly enhance our ability to use this assay in large-scale genetic screens and disease modeling. The software increases the consistency, objectivity, and types of data generated.

A study of shape distributions for estimating histologic grade.

Breast cancers can be histologically categorized (graded) based upon their architectural patterns and cellular types. Inaccurate histologic grading can result in inappropriate treatment for a given patient. Computational analysis of breast cancers offers an operator-independent method for histologic grading that should enhance grading reliability. We present the initial efforts to develop computational technologies that may be used to automatically and objectively estimate the histologic grade of breast cancer tumors. The approach utilizes image processing and shape analysis of imaged histologic sections. Our work is based on the hypothesis that cellular structures found in breast cancer tumors can be transformed into distinct high-resolution shape distributions using geometric measures from stochastic geometry. The resulting shape distributions define well-populated regions of the associated high-dimensional space. Mapping an unknown breast cancer sample into this high-D space and determining to which region it belongs will allow for the automatic estimation of its histologic grade.