The Organoid Counting algorithm examines organoids of varying shape and size using brightfield image analysis.
Currently, manual organoid counting is a routine procedure for many research laboratories but is time-consuming and user-dependent. In addition to organoid counting, it is critical to measure the size and size distribution of organoids in a sample, which can also be subject to user-dependent variation. To overcome these issues, CytoSMART has introduced an organoid counting software that is:
- Accurate - artificial intelligence (AI)-powered image analysis minimizes user-to-user variability and increases count reproducibility
- Multi-informative - the algorithm assesses the number of organoids in a sample, as well as the organoid size and size distribution
- Fast - the software can analyze a single image in less than three seconds* utilizing the CytoSMART™ Cloud (*at a 73 Mbps download and 20 Mbps upload speed)
The CytoSMART Organoid Counting Algorithm is available for:
State-of-the-art, machine learning algorithm for rapid organoid detection
The CytoSMART Organoid Counting software is powered by an image analysis algorithm optimized for organoid detection. The multi-count functionality allows the user to take up to 8 images per sample. This way the sample volume is increased, which highly increases the accuracy of your data. Users obtain information on the quantity and size of the organoids for each individual image, as well as an average of all your images. This data is displayed in separate interfaces that provide a clear overview of the characteristics of the organoid population, including:
- Organoid size distribution
- Sample concentration
- Individual images of the sample
Evaluation of organoid size and concentration
Accurate and reproducible organoid counts
Organoid size can be highly variable. If a large organoid covers the entire field of view, and a single image of that large organoid is used for the concentration estimation, then the smaller organoids that surround it, are neglected and organoid concentration of the sample is underestimated. Users can overcome this issue by taking multiple images of the sample at various positions within the counting chamber. Using the data obtained from multiple images, the accuracy of the organoid count drastically improves. The organoid size can be an important property for further experiments. The histogram of the organoid area allows the users a better understanding of the size distribution of organoids within your sample.
Counting organoids of varying shape
Accurate and reproducible organoid counts
The shape of organoids is dependent on a variety of factors, including the physical features of cells that form organoids, cell proliferation rates, and cell differentiation capacity. Also, the culture conditions can highly influence the morphology of your organoids. Based on the morphological differences between organoids, the CytoSMART Automated Organoid Counting software currently has two different image analysis algorithms available for the analysis of your organoids. The first algorithm is optimized for the analysis of spherical organoids. The high sensitivity of the algorithm results in better differentiation between cell debris and organoids, which reduces the number of false positives/false negatives. In addition to this, the other algorithm is optimized for the detection and analysis of irregularly shaped organoids, producing highly accurate organoid size measurements.
Accessible data anywhere, anytime
The Organoid Counting software instantly generates a report containing organoid number and size. The reports are then sent to the CytoSMART™ Cloud, which enables you to access the analyzed image data on your smartphone, tablet, or computer at any time.
Learn more about the CytoSMART automated cell and organoid counters
|Organoid size range||20 to 200 μm|
|Measurement time**||< 3 sec|
|Counting chamber||Reusable and disposable counting chambers with 0.1 or 0.2 mm height|
|Sample volume||10 μl|
|Field of view||1.5 mm × 1.5 mm|
|Image resolution||1536 × 1536 pixels|
|** at 73 Mbps download and 20 Mbps upload speed|
|Research use only. Not intended for diagnostic purposes|