An incremental approach to automated protein localisation

Tscherepanow M, Jensen N, Kummert F. An incremental approach to automated protein localisation. BMC Bioinformatics. 2008;9(1): 445.Background: The subcellular localisation of proteins in intact living cells is an important means for gaining information about protein functions. Even dynamic processes can be captured, which can barely be predicted based on amino acid sequences. Besides increasing our knowledge about intracellular processes, this information facilitates the development of innovative therapies and new diagnostic methods. In order to perform such a localisation, the proteins under analysis are usually fused with a fluorescent protein. So, they can be observed by means of a fluorescence microscope and analysed. In recent years, several automated methods have been proposed for performing such analyses. Here, two different types of approaches can be distinguished: techniques which enable the recognition of a fixed set of protein locations and methods that identify new ones. To our knowledge, a combination of both approaches – i.e. a technique, which enables supervised learning using a known set of protein locations and is able to identify and incorporate new protein locations afterwards – has not been presented yet. Furthermore, associated problems, e.g. the recognition of cells to be analysed, have usually been neglected. Results: We introduce a novel approach to automated protein localisation in living cells. In contrast to well-known techniques, the protein localisation technique presented in this article aims at combining the two types of approaches described above: After an automatic identification of unknown protein locations, a potential user is enabled to incorporate them into the pre-trained system. An incremental neural network allows the classification of a fixed set of protein location as well as the detection, clustering and incorporation of additional patterns that occur during an experiment. Here, the proposed technique achieves promising results with respect to both tasks. In addition, the protein localisation procedure has been adapted to an existing cell recognition approach. Therefore, it is especially well-suited for high-throughput investigations where user interactions have to be avoided. Conclusion: We have shown that several aspects required for developing an automatic protein localisation technique – namely the recognition of cells, the classification of protein distribution patterns into a set of learnt protein locations, and the detection and learning of new locations – can be combined successfully. So, the proposed method constitutes a crucial step to render image-based protein localisation techniques amenable to large-scale experiments

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

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An Extended TopoART Network for the Stable On-Line Learning of Regression Functions

Tscherepanow M. An Extended TopoART Network for the Stable On-Line Learning of Regression Functions. In: Lu B-L, Zhang L, Kwok J, eds. Neural Information Processing : 18th International Conference, ICONIP 2011, November 13-17, 2011, Proceedings, Part II. Lecture notes in computer science, 7063. Berlin: Springer; 2011: 562-571.In this paper, a novel on-line regression method is presented. Due to its origins in Adaptive Resonance Theory neural networks, this method is particularly well-suited to problems requiring stable incremental learning. Its performance on five publicly available datasets is shown to be at least comparable to two established off-line methods. Furthermore, it exhibits considerable improvements in comparison to its closest supervised relative Fuzzy ARTMAP

Subcellular Localisation of Proteins in Living Cells Using a Genetic Algorithm and an Incremental Neural Network

Tscherepanow M, Kummert F. Subcellular Localisation of Proteins in Living Cells Using a Genetic Algorithm and an Incremental Neural Network. In: Horsch A, Deserno TM, Handels H, Meinzer H-P, Tolxdorff T, eds. Proceedings of the Workshop 'Bildverarbeitung für die Medizin' (BVM). Berlin: Springer; 2007: 11-15.The subcellular localisation of proteins in living cells is a crucial means for the determination of their function. We propose an approach to realise such a protein localisation based on microscope images. In order to reach this goal, appropriate features are selected. Then, the initial feature set is optimised by a genetic algorithm. The actual classification of possible protein localisations is accomplished by an incremental neural network which not only achieves a very high accuracy, but enables on-line learning, as well

ART-based Fusion of Multi-Modal Information for Mobile Robots

Berghöfer E, Schulze D, Tscherepanow M, Wachsmuth S. ART-based Fusion of Multi-Modal Information for Mobile Robots. In: Iliadis L, Jayne C, eds. Proceedings of the International Conference on Engineering Applications of Neural Networks (EANN). IFIP Advances in Information and Communication Technology. Vol 363. Berlin: Springer; 2011: 1-10.Robots operating in complex environments shared with humans are confronted with numerous problems. One important problem is the identification of obstacles and interaction partners. In order to reach this goal, it can be beneficial to use data from multiple available sources, which need to be processed appropriately. Furthermore, such environments are not static. Therefore, the robot needs to learn novel objects. In this paper, we propose a method for learning and identifying obstacles based on multi-modal information. As this approach is based on Adaptive Resonance Theory networks, it is inherently capable of incremental online learning

Long-term tracking of budding yeast cells in brightfield microscopy: CellStar and the Evaluation Platform

International audienceWith the continuous expansion of single cell biology, the observation of the behaviour of individual cells over extended durations and with high accuracy has become a problem of central importance. Surprisingly, even for yeast cells that have relatively regular shapes, no solution has been proposed that reaches the high quality required for long-term experiments for segmentation and tracking (S&T) based on brightfield images. Here, we present CellStar, a tool chain designed to achieve good performance in long-term experiments. The key features are the use of a new variant of parametrized active rays for seg-mentation, a neighbourhood-preserving criterion for tracking, and the use of an iterative approach that incrementally improves S&T quality. A graphical user interface enables manual corrections of S&T errors and their use for the automated correction of other, related errors and for parameter learning. We created a benchmark dataset with manually analysed images and compared CellStar with six other tools, showing its high performance, notably in long-term tracking. As a community effort, we set up a website, the Yeast Image Toolkit, with the benchmark and the Evaluation Platform to gather this and additional information provided by others

Microscopic Cell Nuclei Segmentation Based on Adaptive Attention Window

This paper presents an adaptive attention window (AAW)-based microscopic cell nuclei segmentation method. For semantic AAW detection, a luminance map is used to create an initial attention window, which is then reduced close to the size of the real region of interest (ROI) using a quad-tree. The purpose of the AAW is to facilitate background removal and reduce the ROI segmentation processing time. Region segmentation is performed within the AAW, followed by region clustering and removal to produce segmentation of only ROIs. Experimental results demonstrate that the proposed method can efficiently segment one or more ROIs and produce similar segmentation results to human perception. In future work, the proposed method will be used for supporting a region-based medical image retrieval system that can generate a combined feature vector of segmented ROIs based on extraction and patient data