Seungil Huh

Automated Mitosis Detection of Stem Cell Populations in Phase-Contrast Microscopy Images

Due to the enormous potential and impact that stem cells may have on regenerative medicine, there has been a rapidly growing interest for tools to analyze and characterize the behaviors of these cells in vitro in an automated and high throughput fashion. Among these behaviors, mitosis, or cell division, is important since stem cells proliferate and renew themselves through mitosis. However, current automated systems for measuring cell proliferation often require destructive or sacrificial methods of cell manipulation such as cell lysis or in vitro staining. In this paper, we propose an effective approach for automated mitosis detection using phase-contrast time-lapse microscopy, which is a nondestructive imaging modality, thereby allowing continuous monitoring of cells in culture. In our approach, we present a probabilistic model for event detection, which can simultaneously 1) identify spatio-temporal patch sequences that contain a mitotic event and 2) localize a birth event, defined as the time and location at which cell division is completed and two daughter cells are born. Our approach significantly outperforms previous approaches in terms of both detection accuracy and computational efficiency, when applied to multipotent C3H10T1/2 mesenchymal and C2C12 myoblastic stem cell populations.

Cell segmentation in phase contrast microscopy images via semi-supervised classification over optics-related features.

Phase-contrast microscopy is one of the most common and convenient imaging modalities to observe long-term multi-cellular processes, which generates images by the interference of lights passing through transparent specimens and background medium with different retarded phases. Despite many years of study, computer-aided phase contrast microscopy analysis on cell behavior is challenged by image qualities and artifacts caused by phase contrast optics. Addressing the unsolved challenges, the authors propose (1) a phase contrast microscopy image restoration method that produces phase retardation features, which are intrinsic features of phase contrast microscopy, and (2) a semi-supervised learning based algorithm for cell segmentation, which is a fundamental task for various cell behavior analysis. Specifically, the image formation process of phase contrast microscopy images is first computationally modeled with a dictionary of diffraction patterns; as a result, each pixel of a phase contrast microscopy image is represented by a linear combination of the bases, which we call phase retardation features. Images are then partitioned into phase-homogeneous atoms by clustering neighboring pixels with similar phase retardation features. Consequently, cell segmentation is performed via a semi-supervised classification technique over the phase-homogeneous atoms. Experiments demonstrate that the proposed approach produces quality segmentation of individual cells and outperforms previous approaches.

Discriminative topic modeling based on manifold learning

Topic modeling has been popularly used for data analysis in various domains including text documents. Previous topic models, such as probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA), have shown impressive success in discovering low-rank hidden structures for modeling text documents. These models, however, do not take into account the manifold structure of data, which is generally informative for the non-linear dimensionality reduction mapping. More recent models, namely Laplacian PLSI (LapPLSI) and Locally-consistent Topic Model (LTM), have incorporated the local manifold structure into topic models and have shown the resulting benefits. But these approaches fall short of the full discriminating power of manifold learning as they only enhance the proximity between the low-rank representations of neighboring pairs without any consideration for non-neighboring pairs. In this paper, we propose Discriminative Topic Model (DTM) that separates non-neighboring pairs from each other in addition to bringing neighboring pairs closer together, thereby preserving the global manifold structure as well as improving the local consistency. We also present a novel model fitting algorithm based on the generalized EM and the concept of Pareto improvement. As a result, DTM achieves higher classification performance in a semi-supervised setting by effectively exposing the manifold structure of data. We provide empirical evidence on text corpora to demonstrate the success of DTM in terms of classification accuracy and robustness to parameters compared to state-of-the-art techniques.

Detection of mitosis within a stem cell population of high cell confluence in phase-contrast microscopy images

Computer vision analysis of cells in phase-contrast microscopy images enables long-term continuous monitoring of live cells, which has not been feasible using the existing cellular staining methods due to the use of fluorescence reagents or fixatives. In cell culture analysis, accurate detection of mitosis, or cell division, is critical for quantitative study of cell proliferation. In this work, we present an approach that can detect mitosis within a cell population of high cell confluence, or high cell density, which has proven challenging because of the difficulty in separating individual cells. We first detect the candidates for birth events that are defined as the time and location at which mitosis is complete and two daughter cells first appear. Each candidate is then examined whether it is real or not after incorporating spatio-temporal information by tracking the candidate in the neighboring frames. For the examination, we design a probabilistic model named Two-Labeled Hidden Conditional Random Field (TL-HCRF) that can use the information on the timing of the candidate birth event in addition to the visual change of cells over time. Applied to two cell populations of high cell confluence, our method considerably outperforms previous methods. Comparisons with related statistical models also show the superiority of TL-HCRF on the proposed task.

Efficient framework for automated classification of subcellular patterns in budding yeast

Fluorescent‐tagging and digital imaging are widely used to determine the subcellular location of proteins. An extensive publicly available collection of images for most proteins expressed in the yeast S. cerevisae has provided both an important source of information on protein location but also a testbed for methods designed to automate the assignment of locations to unknown proteins. The first system for automated classification of subcellular patterns in these yeast images utilized a computationally expensive method for segmentation of images into individual cells and achieved an overall accuracy of 81%. The goal of the present study was to improve on both the computational efficiency and accuracy of this task. Numerical features derived from applying Gabor filters to small image patches were implemented so that patterns could be classified without segmentation into single cells. When tested on 20 classes of images visually classified as showing a single subcellular pattern, an overall accuracy of 87.8% was achieved, with 2330 images out of 2655 images in the UCSF dataset being correctly classified. On the 4 largest classes of these images, 95.3% accuracy was achieved. The improvement over the previous approach is not only in classification accuracy but also in computational efficiency, with the new approach taking about 1 h on a desktop computer to complete all steps required to perform a 6‐fold cross validation on all images.

Phase Contrast Image Restoration via Dictionary Representation of Diffraction Patterns

The restoration of microscopy images makes the segmentation and detection of cells easier and more reliable, which facilitates automated cell tracking and cell behavior analysis. In this paper, the authors analyze the image formation process of phase contrast images and propose an image restoration method based on the dictionary representation of diffraction patterns. By formulating and solving a min-l1 optimization problem, each pixel is restored into a feature vector corresponding to the dictionary representation. Cells in the images are then segmented by the feature vector clustering. In addition to segmentation, since the feature vectors capture the information on the phase retardation caused by cells, they can be used for cell stage classification between intermitotic and mitotic/apoptotic stages. Experiments on three image sequences demonstrate that the dictionary-based restoration method can restore phase contrast images containing cells with different optical natures and provide promising results on cell stage classification.

Apoptosis detection for adherent cell populations in time-lapse phase-contrast microscopy images

The detection of apoptosis, or programmed cell death, is important to understand the underlying mechanism of cell development. At present, apoptosis detection resorts to fluorescence or colorimetric assays, which may affect cell behavior and thus not allow long-term monitoring of intact cells. In this work, we present an image analysis method to detect apoptosis in time-lapse phase-contrast microscopy, which is nondestructive imaging. The method first detects candidates for apoptotic cells based on the optical principle of phase-contrast microscopy in connection with the properties of apoptotic cells. The temporal behavior of each candidate is then examined in its neighboring frames in order to determine if the candidate is indeed an apoptotic cell. When applied to three C2C12 myoblastic stem cell populations, which contain more than 1000 apoptosis, the method achieved around 90% accuracy in terms of average precision and recall.

Discriminative Topic Modeling Based on Manifold Learning

Topic modeling has become a popular method used for data analysis in various domains including text documents. Previous topic model approaches, such as probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA), have shown impressive success in discovering low-rank hidden structures for modeling text documents. These approaches, however do not take into account the manifold structure of the data, which is generally informative for nonlinear dimensionality reduction mapping. More recent topic model approaches, Laplacian PLSI (LapPLSI) and Locally-consistent Topic Model (LTM), have incorporated the local manifold structure into topic models and have shown resulting benefits. But they fall short of achieving full discriminating power of manifold learning as they only enhance the proximity between the low-rank representations of neighboring pairs without any consideration for non-neighboring pairs. In this article, we propose a new approach, Discriminative Topic Model (DTM), which separates non-neighboring pairs from each other in addition to bringing neighboring pairs closer together, thereby preserving the global manifold structure as well as improving local consistency. We also present a novel model-fitting algorithm based on the generalized EM algorithm and the concept of Pareto improvement. We empirically demonstrate the success of DTM in terms of unsupervised clustering and semisupervised classification accuracies on text corpora and robustness to parameters compared to state-of-the-art techniques.

An Engineered Approach to Stem Cell Culture: Automating the Decision Process for Real-Time Adaptive Subculture of Stem Cells

Current cell culture practices are dependent upon human operators and remain laborious and highly subjective, resulting in large variations and inconsistent outcomes, especially when using visual assessments of cell confluency to determine the appropriate time to subculture cells. Although efforts to automate cell culture with robotic systems are underway, the majority of such systems still require human intervention to determine when to subculture. Thus, it is necessary to accurately and objectively determine the appropriate time for cell passaging. Optimal stem cell culturing that maintains cell pluripotency while maximizing cell yields will be especially important for efficient, cost-effective stem cell-based therapies. Toward this goal we developed a real-time computer vision-based system that monitors the degree of cell confluency with a precision of 0.791±0.031 and recall of 0.559±0.043. The system consists of an automated phase-contrast time-lapse microscope and a server. Multiple dishes are sequentially imaged and the data is uploaded to the server that performs computer vision processing, predicts when cells will exceed a pre-defined threshold for optimal cell confluency, and provides a Web-based interface for remote cell culture monitoring. Human operators are also notified via text messaging and e-mail 4 hours prior to reaching this threshold and immediately upon reaching this threshold. This system was successfully used to direct the expansion of a paradigm stem cell population, C2C12 cells. Computer-directed and human-directed control subcultures required 3 serial cultures to achieve the theoretical target cell yield of 50 million C2C12 cells and showed no difference for myogenic and osteogenic differentiation. This automated vision-based system has potential as a tool toward adaptive real-time control of subculturing, cell culture optimization and quality assurance/quality control, and it could be integrated with current and developing robotic cell cultures systems to achieve complete automation.

Automatic cell tracking applied to analysis of cell migration in wound healing assay

The wound healing assay in vitro is widely used for research and discovery in biology and medicine. This assay allows for observing the healing process in vitro in which the cells on the edges of the artificial wound migrate toward the wound area. The influence of different culture conditions can be measured by observing the change in the size of the wound area. For further investigation, more detailed measurements of the cell behaviors are required. In this paper, we present an application of automatic cell tracking in phase-contrast microscopy images to wound healing assay. The cell behaviors under three different culture conditions have been analyzed. Our cell tracking system can track individual cells during the healing process and provide detailed spatio-temporal measurements of cell behaviors. The application demonstrates the effectiveness of automatic cell tracking for quantitative and detailed analysis of the cell behaviors in wound healing assay in vitro.