Automated segmentation of tissue images for computerized IHC analysis

This paper presents two automated methods for the segmentation ofimmunohistochemical tissue images that overcome the limitations of themanual approach aswell as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologie

Building a needs-based curriculum in data science and artificial intelligence: case studies in Indonesia, Sri Lanka, and Thailand

Indonesia and Thailand are middle-income countries within the South-East Asia region. They have well-established and growing higher education systems, increasingly focused on quality improvement. However, they fall behind regional leaders in educating people who design, develop, deploy and train data science and artificial intelligence (DS&AI) based technology, as evident from the technological market, regionally dominated by Singapore and Malaysia, while the region as a whole is far behind China. A similar situation holds also for Sri Lanka, in the South Asia region technologically dominated by India. In this paper, we describe the design of a master's level curriculum in data science and artificial intelligence using European experience on building such curricula. The design of such a curriculum is a nontrivial exercise because there is a constant trade-off between having a sufficiently broad academic curriculum and adequately meeting regional needs, including those of industrial stakeholders. In fact, findings from a gap analysis and assessment of needs from three case studies in Indonesia, Sri Lanka, and Thailand comprise the most significant component of our curriculum development process.The authors would like to thank the European Union Erasmus+ programme which provided funding through the Capacity Building Higher Education project on Curriculum Development in Data Science and Artificial Intelligence, registered under the reference number 599600-EPP-1-2018-1-TH-EPPKA2-CBHE-JP

TEMPy: a Python library for assessment of 3D electron microscopy density fits

Three-dimensional electron microscopy (3D EM) is currently one of the most promising techniques used to study macromolecular assemblies. Rigid and flexible fitting of atomic models into density maps is often essential to gain further insights into the assemblies they represent. Currently, tools that facilitate the assessment of fitted atomic models and maps are needed. TEMPy – Template and EM comparison using Python – is a toolkit designed for this purpose. The library includes a set of methods to assess density fits in intermediate-to-low resolution maps, both globally and locally. It also provides procedures for single fit assessment, ensemble generation of fits, clustering, multiple and consensus scoring, as well as plots and output files for visualisation purposes to help the user in analysing rigid and flexible fits. The modular nature of TEMPy helps the integration of scoring and assessment of fits into large pipelines, making it a tool for both novice and expert structural biologists

Digital Image Analysis of Cells : Applications in 2D, 3D and Time

Light microscopes are essential research tools in biology and medicine. Cell and tissue staining methods have improved immensely over the years and microscopes are now equipped with digital image acquisition capabilities. The image data produced require development of specialized analysis methods. This thesis presents digital image analysis methods for cell image data in 2D, 3D and time sequences. Stem cells have the capability to differentiate into specific cell types. The mechanism behind differentiation can be studied by tracking cells over time. This thesis presents a combined segmentation and tracking algorithm for time sequence images of neural stem cells.The method handles splitting and merging of cells and the results are similar to those achieved by manual tracking. Methods for detecting and localizing signals from fluorescence stained biomolecules are essential when studying how they function and interact. A study of Smad proteins, that serve as transcription factors by forming complexes and enter the cell nucleus, is included in the thesis. Confocal microscopy images of cell nuclei are delineated using gradient information, and Smad complexes are localized using a novel method for 3D signal detection. Thus, the localization of Smad complexes in relation to the nuclear membrane can be analyzed. A detailed comparison between the proposed and previous methods for detection of point-source signals is presented, showing that the proposed method has better resolving power and is more robust to noise. In this thesis, it is also shown how cell confluence can be measured by classification of wavelet based texture features. Monitoring cell confluence is valuable for optimization of cell culture parameters and cell harvest. The results obtained agree with visual observations and provide an efficient approach to monitor cell confluence and detect necrosis. Quantitative measurements on cells are important in both cytology and histology. The color provided by Pap (Papanicolaou) staining increases the available image information. The thesis explores different color spaces of Pap smear images from thyroid nodules, with the aim of finding the representation that maximizes detection of malignancies using color information in addition to quantitative morphological parameters. The presented methods provide useful tools for cell image analysis, but they can of course also be used for other image analysis applications

A hybrid algorithm for player arm biomechanics evaluation in outdoor sporting activities

Despite the large amount of studies conducted in the field of human pose estimation and tracking in sports, currently there is a lack of a system which is capable to track player arm movements in real time. Such a system can assist the players to master the right techniques with guaranteed optimal performance of the player. In this paper we propose a robust algorithm to model player arm movements in outdoor sporting activities. The system uses trained cascade object classifier to predict a region of interest for arm in the monocular input video sequence. Optical flow algorithm is employed to extract the motion in that region. Arm region in resultant binary image is later classified using Active Shape Model. The algorithm is tested and validated using several experiments for tracking ball delivery process in cricket as well as for tracking service in volleyball and tennis. The algorithm is capable of classifying and tracking player arm movements with more than 80 percent accuracy, irrespective of the position and background complexities that the real gaming conditions offer.</p

Seeded watersheds for combined segmentation and tracking of cells

Abstract. Watersheds are very powerful for image segmentation, and seeded watersheds have shown to be useful for object detection in images of cells in vitro. This paper shows that if cells are imaged over time, segmentation results from a previous time frame can be used as seeds for watershed segmentation of the current time frame. The seeds from the previous frame are combined with morphological seeds from the current frame, and over-segmentation is reduced by rule-based merging, propagating labels from one time-frame to the next. Thus, watershed segmentation is used for segmentation as well as tracking of cells over time. The described algorithm was tested on neural stem/progenitor cells imaged using time-lapse microscopy. Tracking results agreed to 71% to manual tracking results. The results were also compared to tracking based on solving the assignment problem using a modified version of the auction algorithm

Comparative study of complexities of genetic algorithms and other hierarchical informed searches

Search Algorithms are widely used in various contexts of computer science, mainly in finding the optimal solution. Some search algorithms are very accurate but computationally complex, thereby resulting limitations in applying them in practical real time applications. In this paper we discuss A* Search, Greedy Search and Genetic Algorithms comparatively with a modified Genetic Algorithm that we introduce, in the application of complex path finding. Simulation results show that Genetic Algorithm and modified Genetic Algorithm are suitable for path finding problems