Olympus scanR high-content screening station v. 3.2 brings improved image quality with award-winning X Line objectives

The scanR high-content screening (HCS) station version 3.2 delivers improved image quality, better quantitative data, support for challenging segmentation and classification applications and increased speed to help life science researchers maximize the information they get from their samples.

Olympus scanR high-content screening station v. 3.2 brings improved image quality with award-winning X Line objectives

Better images for better data

Key to these functionalities is Olympus’ breakthrough lens manufacturing technology. X Line™ objectives offer simultaneously improved chromatic aberration correction, image flatness and resolution. This combination enables the scanR system to more rapidly acquire higher-quality multicolor images while achieving better quantitative results over the full field of view. Since the ability to get quantitative data from the scanR system is directly tied to image quality, X Line objectives help the system extract more information from images.

High signal-to-noise images

The v. 3.2 update brings support for Hamamatsu’s ORCA-Fusion sCMOS camera. The camera’s large 21.2 mm (0.83 in.) sensor chip captures more sample information with high quantum efficiency and sensitivity. This results in higher-quality images with minimal noise.

Expanded deep learning functionality

Using a self-learning microscopy approach, the scanR system’s AI functionality automatically analyzes data by incorporating a learned analysis protocol into its assay-based workflow. This improved functionality adds support for multi-class semantic segmentation, which enables it to tackle some of the most challenging segmentation and classification challenges in life science microscopy. Brightfield analysis, ultra-low signal quantification, label-free mitosis assays and the classification of complex phenotypes are all possible using scanR deep-learning technology.

These improvements in optics, camera support and software make the new scanR 3.2 highly suitable for high-throughput imaging and image analysis across a range of life science disciplines. It helps ensure that researchers can analyze large studies quickly, efficiently and without compromising on image or data quality.

For more information about the scanR high-content screening station, visit olympus-lifescience.com/microscopes/inverted/scanr/

Citations

Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Evident Corporation - Life Sciences. (2020, July 20). Olympus scanR high-content screening station v. 3.2 brings improved image quality with award-winning X Line objectives. News-Medical. Retrieved on November 23, 2024 from https://www.news-medical.net/news/20200720/Olympus-scanR-high-content-screening-station-v-32-brings-improved-image-quality-with-award-winning-X-Line-objectives.aspx.

  • MLA

    Evident Corporation - Life Sciences. "Olympus scanR high-content screening station v. 3.2 brings improved image quality with award-winning X Line objectives". News-Medical. 23 November 2024. <https://www.news-medical.net/news/20200720/Olympus-scanR-high-content-screening-station-v-32-brings-improved-image-quality-with-award-winning-X-Line-objectives.aspx>.

  • Chicago

    Evident Corporation - Life Sciences. "Olympus scanR high-content screening station v. 3.2 brings improved image quality with award-winning X Line objectives". News-Medical. https://www.news-medical.net/news/20200720/Olympus-scanR-high-content-screening-station-v-32-brings-improved-image-quality-with-award-winning-X-Line-objectives.aspx. (accessed November 23, 2024).

  • Harvard

    Evident Corporation - Life Sciences. 2020. Olympus scanR high-content screening station v. 3.2 brings improved image quality with award-winning X Line objectives. News-Medical, viewed 23 November 2024, https://www.news-medical.net/news/20200720/Olympus-scanR-high-content-screening-station-v-32-brings-improved-image-quality-with-award-winning-X-Line-objectives.aspx.

Comments

The opinions expressed here are the views of the writer and do not necessarily reflect the views and opinions of News Medical.
Post a new comment
Post

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.

You might also like...
AI-based pathology diagnosis tool in development detects 7 types of gastric cancer