Fast automated cell phenotype image classification

BACKGROUND: The genomic revolution has led to rapid growth in sequencing of genes and proteins, and attention is now turning to the function of the encoded proteins. In this respect, microscope imaging of a protein's sub-cellular localisation is proving invaluable, and recent advances in automated fluorescent microscopy allow protein localisations to be imaged in high throughput. Hence there is a need for large scale automated computational techniques to efficiently quantify, distinguish and classify sub-cellular images. While image statistics have proved highly successful in distinguishing localisation, commonly used measures suffer from being relatively slow to compute, and often require cells to be individually selected from experimental images, thus limiting both throughput and the range of potential applications. Here we introduce threshold adjacency statistics, the essence which is to threshold the image and to count the number of above threshold pixels with a given number of above threshold pixels adjacent. These novel measures are shown to distinguish and classify images of distinct sub-cellular localization with high speed and accuracy without image cropping. RESULTS: Threshold adjacency statistics are applied to classification of protein sub-cellular localization images. They are tested on two image sets (available for download), one for which fluorescently tagged proteins are endogenously expressed in 10 sub-cellular locations, and another for which proteins are transfected into 11 locations. For each image set, a support vector machine was trained and tested. Classification accuracies of 94.4% and 86.6% are obtained on the endogenous and transfected sets, respectively. Threshold adjacency statistics are found to provide comparable or higher accuracy than other commonly used statistics while being an order of magnitude faster to calculate. Further, threshold adjacency statistics in combination with Haralick measures give accuracies of 98.2% and 93.2% on the endogenous and transfected sets, respectively. CONCLUSION: Threshold adjacency statistics have the potential to greatly extend the scale and range of applications of image statistics in computational image analysis. They remove the need for cropping of individual cells from images, and are an order of magnitude faster to calculate than other commonly used statistics while providing comparable or better classification accuracy, both essential requirements for application to large-scale approaches

Principles of Bioimage Informatics: Focus on Machine Learning of Cell Patterns

Abstract. The field of bioimage informatics concerns the development and use of methods for computational analysis of biological images. Traditionally, analysis of such images has been done manually. Manual annotation is, however, slow, expensive, and often highly variable from one expert to another. Furthermore, with modern automated microscopes, hundreds to thousands of images can be collected per hour, making manual analysis infeasible. This field borrows from the pattern recognition and computer vision literature (which contain many techniques for image processing and recognition), but has its own unique challenges and tradeoffs. Fluorescence microscopy images represent perhaps the largest class of biological images for which automation is needed. For this modality, typical problems include cell segmentation, classification of phenotypical response, or decisions regarding differentiated responses (treatment vs. control setting). This overview focuses on the problem of subcellular location determination as a running example, but the techniques discussed are often applicable to other problems.

A multimeric L2 vaccine for prevention of animal papillomavirus infections

It is unclear what level of neutralizing antibody is sufficient to protect cattle from experimental bovine papillomavirus type 4 (BPV4) challenge. Markedly lower, and often undetected, serum neutralizing antibody titers were associated with protection in cattle vaccinated with BPV4 12 as compared to L1 VLP. We hypothesized that vaccination with concatemers of the N-terminal protective epitopes of 12 derived from multiple animal papillomavirus types would enhance the breadth and strength of immunity. Therefore we generated a multimeric 12 antigen derived from three bovine and three canine papillomavirus types with divergent phenotypes and purified it from bacteria. Mice vaccinated three times with this six type 12 vaccine formulated in alum or RIBI adjuvant generated robust serum neutralizing antibody titers against BPV1, BPV4 and canine oral papillomavirus (COPV). Furthermore, vaccination with this six type 12 vaccine formulated in adjuvant, like BPV1 L1 VLP, protected the mice from experimental challenge with BPV1 pseudovirus

Automatic Segmentation of Unstained Living Cells in Bright-Field Microscope Images

Tscherepanow M, Zöllner F, Hillebrand M, Kummert F. Automatic Segmentation of Unstained Living Cells in Bright-Field Microscope Images. In: Perner P, Salvetti O, eds. Proceedings of the International Conference on Mass-Data Analysis of Images and Signals (MDA). Berlin: Springer; 2008: 158-172.The automatic subcellular localisation of proteins in living cells is a critical step in determining their function. The evaluation of fluorescence images constitutes a common method of localising these proteins. For this, additional knowledge about the position of the considered cells within an image is required. In an automated system, it is advantageous to recognise these cells in bright-field microscope images taken in parallel with the regarded fluorescence micrographs. Unfortunately, currently available cell recognition methods are only of limited use within the context of protein localisation, since they frequently require microscopy techniques that enable images of higher contrast (e.g. phase contrast microscopy or additional dyes) or can only be employed with too low magnifications. Therefore, this article introduces a novel approach to the robust automatic recognition of unstained living cells in bright-field microscope images. Here, the focus is on the automatic segmentation of cells