Sartorius Bioanalytical Instruments, Inc.

Essen Instruments, Inc.

Timothy R Jackson

Nevine Holtz

Rickard Sjögren

Edvin Forsgren

Christoffer Edlund

Methods are provided to project depth-spanning stacks of limited depth-of-field images of a sample into a single image of the sample that can provide in-focus image information about three-dimensional contents of the image. These methods include applying filters to the stacks of images in order to identify pixels within each image that have been captured in focus. These in-focus pixels are then combined to provide the single image of the sample. Filtering of such image stacks can also allow for the determination of depth maps or other geometric information about contents of the sample. Such depth information can also be used to inform segmentation of images of the sample, e.g., by further dividing identified regions that correspond to the contents of the sample at multiple different depths.

Image processing and segmentation of sets of Z-stacked images of three-dimensional biological samples

A method is disclosed for acquiring a single, in-focus two-dimensional projection image of a live, three-dimensional cell culture sample, with a fluorescence microscope. One or more long-exposure 'Z-sweep' images are obtained, i.e. via a single or series of continuous acquisitions, while moving the Z-focal plane of a camera through the sample, to produce one or more two-dimensional images of fluorescence intensity integrated over the Z-dimension. The acquisition method is much faster than a Z-stack method, which enables higher throughput and reduces the risk of exposing the sample to too much fluorescent light. The long-exposure Z-sweep image(s) is then input into a neural network which has been trained to produce a high-quality (in-focus) two-dimensional projection image of the sample. With these high-quality projection images, biologically relevant analysis metrics can be obtained to describe the fluorescence signal using standard image analysis techniques, such as fluorescence object count and other fluorescence intensity metrics (e.g., mean intensity, texture, etc.).

Method and system for acquisition of fluorescence images of live-cell biological samples

The disclosure provides example methods that include a processor: (a) generating at least one phase contrast image of a biological specimen comprising one or more cells centered around a focal plane for the biological specimen; (b) generating a confluence mask in the form of a binary image based on the at least one phase contrast image; (c) receiving a first brightfield image of the biological specimen at a defocusing distance above the focal plane and a second brightfield image of the biological specimen at the defocusing distance below the focal plane; (d) generating a cell image of the biological specimen based on the first and second brightfield image; (e) generating a seed mask based on the cell image and the phase contrast image; and (f) generating an image of the biological specimen showing a cell-by-cell segmentation mask based on the seed mask and the confluence mask.

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Ann Arbor, MI, United States of America

Timothy R Jackson