Spatiotemporal Identification of Cell Divisions Using Symmetry Properties in Time-Lapse Phase Contrast Microscopy

A variety of biological and pharmaceutical studies, such as for anti-cancer drugs, require the quantification of cell responses over long periods of time. This is performed with time-lapse video microscopy that gives a long sequence of frames. For this purpose, phase contrast imaging is commonly used since it is minimally invasive. The cell responses of interest in this study are the mitotic cell divisions. Their manual measurements are tedious, subjective, and restrictive. This study introduces an automated method for these measurements. The method starts with preprocessing for restoration and reconstruction of the phase contrast time-lapse sequences. The data are first restored from intensity non-uniformities. Subsequently, the circular symmetry of the contour of the mitotic cells in phase contrast images is used by applying a Circle Hough Transform (CHT) to reconstruct the entire cells. The CHT is also enhanced with the ability to “vote” exclusively towards the center of curvature. The CHT image sequence is then registered for misplacements between successive frames. The sequence is subsequently processed to detect cell centroids in individual frames and use them as starting points to form spatiotemporal trajectories of cells along the positive as well as along the negative time directions, that is, anti-causally. The connectivities of different trajectories enhanced by the symmetry of the trajectories of the daughter cells provide as topological by-products the events of cell divisions together with the corresponding entries into mitoses as well as exits from cytokineses. The experiments use several experimental video sequences from three different cell lines with many cells undergoing mitoses and divisions. The quantitative validations of the results of the processing demonstrate the high performance and efficiency of the method

Detecting cells and analyzing their behaviors in microscopy images using deep neural networks

The computer-aided analysis in the medical imaging field has attracted a lot of attention for the past decade. The goal of computer-vision based medical image analysis is to provide automated tools to relieve the burden of human experts such as radiologists and physicians. More specifically, these computer-aided methods are to help identify, classify and quantify patterns in medical images. Recent advances in machine learning, more specifically, in the way of deep learning, have made a big leap to boost the performance of various medical applications. The fundamental core of these advances is exploiting hierarchical feature representations by various deep learning models, instead of handcrafted features based on domain-specific knowledge. In the work presented in this dissertation, we are particularly interested in exploring the power of deep neural network in the Circulating Tumor Cells detection and mitosis event detection. We will introduce the Convolutional Neural Networks and the designed training methodology for Circulating Tumor Cells detection, a Hierarchical Convolutional Neural Networks model and a Two-Stream Bidirectional Long Short-Term Memory model for mitosis event detection and its stage localization in phase-contrast microscopy images”--Abstract, page iii

Opportunities and challenges for deep learning in cell dynamics research

With the growth of artificial intelligence (AI), there has been an increase in the adoption of computer vision and deep learning (DL) techniques for the evaluation of microscopy images and movies. This adoption has not only addressed hurdles in quantitative analysis of dynamic cell biological processes, but it has also started supporting advances in drug development, precision medicine and genome-phenome mapping. Here we survey existing AI-based techniques and tools, and open-source datasets, with a specific focus on the computational tasks of segmentation, classification, and tracking of cellular and subcellular structures and dynamics. We summarise long-standing challenges in microscopy video analysis from the computational perspective and review emerging research frontiers and innovative applications for deep learning-guided automation for cell dynamics research

Attention mechanism in deep neural networks for computer vision tasks

“Attention mechanism, which is one of the most important algorithms in the deep Learning community, was initially designed in the natural language processing for enhancing the feature representation of key sentence fragments over the context. In recent years, the attention mechanism has been widely adopted in solving computer vision tasks by guiding deep neural networks (DNNs) to focus on specific image features for better understanding the semantic information of the image. However, the attention mechanism is not only capable of helping DNNs understand semantics, but also useful for the feature fusion, visual cue discovering, and temporal information selection, which are seldom researched. In this study, we take the classic attention mechanism a step further by proposing the Semantic Attention Guidance Unit (SAGU) for multi-level feature fusion to tackle the challenging Biomedical Image Segmentation task. Furthermore, we propose a novel framework that consists of (1) Semantic Attention Unit (SAU), which is an advanced version of SAGU for adaptively bringing high-level semantics to mid-level features, (2) Two-level Spatial Attention Module (TSPAM) for discovering multiple visual cues within the image, and (3) Temporal Attention Module (TAM) for temporal information selection to solve the Videobased Person Re-identification task. To validate our newly proposed attention mechanisms, extensive experiments are conducted on challenging datasets. Our methods obtain competitive performance and outperform state-of-the-art methods. Selective publications are also presented in the Appendix”--Abstract, page iii

IEEE Access Special Section Editorial: Advanced Signal Processing Methods in Medical Imaging

Medical Imaging is a technique to create visual representations of the interior of the body, with the aim of making accurate diagnoses and optimized treatments. Many medical imaging techniques are widely used to produce images, such as computer tomography (CT), ultrasound (US), positron emission tomography (PET), single photon emission computed tomography (SPECT), and magnetic resonance imaging (MRI)/functional MRI (fMRI)