We present a diffraction-phase and fluorescence 3D microscope as novel bimodal imaging technique, which provides simultaneous phase and multi-color epi-fluorescence acquisitions of living multicellular samples. The instrument consists of an LED-array to acquire intensity images at different illumination angles and an epifluorescence setup for fluorescence excitation. The 3D sample’s optical properties are reconstructed using the beam propagation method embedded inside a deep learning framework. To obtain the fluorescence reconstructions, we developed a novel incoherent model that takes into account the heterogeneous refractive indexes of the scattering sample. We validated the technique on long-term acquisitions of mouse embryos and 3D liver organoids under physiological conditions.
We present a learning-enabled lens-free microscope for quantitative analysis of cell cultures. Leveraging the advances of recent years in learning algorithms, we developed a suite of neural networks that detect, quantify and track the cells. The detection algorithm locates the cells. The quantification algorithm, measures different cell metrics directly from cell phase image patches centred on the cells detections. Measured features include among others: cell morphology (dry mass, thickness, aspect ratio, ...) and local neighbourhood (density, contact surface, …). Finally, the tracking algorithm predicts the position of a given cell at next time point, making it possible to monitor a cell across time. To train these models we designed a semi-automated pipeline able to generate a supervised training datasets of up to millions of cells. The measurements obtained from the proposed method open up for modelling the cell cultures and providing biological insights.
Cellular heterogeneity is the hallmark of many cancers, referring to the co-existence of different phenotypes with very distinct biological behaviours in single isolates. Automatically detecting single-cell heterogeneity is therefore critical, and can provide important information on cancer initiation. We present a clustering algorithm that allows identifying heterogeneity in cell culture from time-lapses of lensless microscopic images. A preliminary segmentation and tracking pipeline extract quantitative features (morphology, motility and reproduction cycle) for each cell. An unsupervised learning algorithm then clusters the time-series of the cell tracks measurements, in two steps. We validate our approach on co-cultures of mixed cells lines, and on murine fibroblasts isolated from genetically modified mice, where the modified genome promotes the establishment of cancers and heterogeneous cell morphologies and behaviours
One of the directions of development in quantitative phase imaging is to provide the capability to reconstruct the phase or preferably refractive index (RI) distribution within thick, highly scattering samples. This direction coincides with current trends in biology, where three-dimensional (3D) organoids are currently replacing standard 2D cultures as more physiological models for tissue growth and organ formation in a dish. The biological complexity of these 3D structures makes the imaging and RI reconstruction particularly challenging, and thus calibration as well as validation structures are important and sought-after tools in instrumentation development. For this reason, in this work, we present the full preparation and measurement procedure for organoid phantoms printed with two-photon polymerization along with the method to obtain the ground truth of the object structure independently of RI reconstruction errors and artifacts.
We present a CNN-based quantification pipeline for the imaging and analysis of adherent cell cultures. The imaging part features two CNNs dedicated to lens-free microscopy performing accelerated holographic reconstruction and phase unwrapping. The analysis part features CNNs estimating several cellular metrics. These CNNs maps phase image into 2D quantitative representations of cell positions and measurements. The outputs images are processed by a local maxima algorithm to obtain a list of cell measurements. Here, we discuss the performance and limitations of this CNN-based quantification pipeline. The advantage is the fast processing time, i.e. the analysis of ~10.000 cells in 10 seconds.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.