Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations.

Phase contrast time-lapse microscopy is a non-destructive technique that generates large volumes of image-based information to quantify the behaviour of individual cells or cell populations. To guide the development of algorithms for computer-aided cell tracking and analysis, 48 time-lapse image sequences, each spanning approximately 3.5 days, were generated with accompanying ground truths for C2C12 myoblast cells cultured under 4 different media conditions, including with fibroblast growth factor 2 (FGF2), bone morphogenetic protein 2 (BMP2), FGF2 + BMP2, and control (no growth factor).

Interactive Cell Segmentation Based on Active and Semi-Supervised Learning.

Automatic cell segmentation can hardly be flawless due to the complexity of image data particularly when time-lapse experiments last for a long time without biomarkers. To address this issue, we propose an interactive cell segmentation method by classifying feature-homogeneous superpixels into specific classes, which is guided by human interventions. Specifically, we propose to actively select the most informative superpixels by minimizing the expected prediction error which is upper bounded by the transductive Rademacher complexity, and then query for human annotations.

Discriminative data transform for image feature extraction and classification.

Good feature design is important to achieve effective image classification. This paper presents a novel feature design with two main contributions. First, prior to computing the feature descriptors, we propose to transform the images with learning-based filters to obtain more representative feature descriptors.

Apoptosis detection for non-adherent cells in time-lapse phase contrast microscopy.

This paper proposes a vision-based method for detecting apoptosis (programmed cell death), which is essential for non-perturbative monitoring of cell expansion. Our method targets non-adherent cells, which float or are suspended freely in the culture medium-in contrast to adherent cells, which are attached to a petri dish. The method first detects cell regions and tracks them over time, resulting in the construction of cell tracklets.

Efficient phase contrast microscopy restoration applied for muscle myotube detection.

This paper proposes a new image restoration method for phase contrast microscopy as a mean to enhance the quality of images prior to image analysis. Compared to state-of-the-art image restoration algorithms, our method has a more solid theoretical foundation and is orders of magnitude more efficient in computation. We validated the proposed method by applying it to automated muscle myotube detection, a challenging problem that has not been tackled without staining images.

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.

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.

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.

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.

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.

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.

Linear discriminant analysis for signatures.

We propose signature linear discriminant analysis (signature-LDA) as an extension of LDA that can be applied to signatures, which are known to be more informative representations of local image features than vector representations, such as visual word histograms. Based on earth mover's distances between signatures, signature-LDA does not require vectorization of local image features in contrast to LDA, which is one of the main limitations of classical LDA. Therefore, signature-LDA minimizes the loss of intrinsic information of local image features while selecting more discriminating features using label information.

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.