JPWO2021072408A5 - - Google Patents
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- JPWO2021072408A5 JPWO2021072408A5 JP2022521445A JP2022521445A JPWO2021072408A5 JP WO2021072408 A5 JPWO2021072408 A5 JP WO2021072408A5 JP 2022521445 A JP2022521445 A JP 2022521445A JP 2022521445 A JP2022521445 A JP 2022521445A JP WO2021072408 A5 JPWO2021072408 A5 JP WO2021072408A5
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- ultraviolet
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Claims (19)
前記光源から出力された前記光線によって照らす前記生体サンプルを受容する受容空間と、
前記生体サンプルと相互作用した光を収集し、その収集した光を撮像装置に中継するように構成されている紫外線イメージング対物レンズと、
前記紫外線イメージング対物レンズからの前記中継された光の画像を撮像するための紫外線感応撮像装置と、を備え、
前記生体サンプルの1または複数の画像を前記紫外線波長のうち1つまたは複数で撮像するように構成されており、かつ、
前記生体サンプル内の1または複数の細胞の分類または特徴付けを行うために前記生体サンプルの1または複数の画像を分析する、深紫外線イメージングシステム。 a light source that outputs a light beam containing an ultraviolet wavelength for illuminating the biological sample;
a receiving space that receives the biological sample illuminated by the light beam output from the light source;
an ultraviolet imaging objective configured to collect light that interacts with the biological sample and relay the collected light to an imaging device;
a UV-sensitive imager for capturing an image of the relayed light from the UV imaging objective;
configured to capture one or more images of the biological sample at one or more of the ultraviolet wavelengths , and
A deep ultraviolet imaging system that analyzes one or more images of the biological sample to classify or characterize one or more cells within the biological sample.
前記光源は、非紫外線波長をさらに含む前記光線を出力するように構成された広帯域光源を備え、
前記バンドパスフィルタのうち1または複数は、前記光源から出力された前記光線をフィルタリングして非紫外線波長を除去し、紫外線波長を中継するように構成されている、請求項1に記載の深紫外線イメージングシステム。 further comprising one or more bandpass filters disposed downstream of the light source,
the light source comprises a broadband light source configured to output the light beam further including non -ultraviolet wavelengths;
The deep ultraviolet light of claim 1, wherein one or more of the bandpass filters are configured to filter the light beam output from the light source to remove non-ultraviolet wavelengths and relay ultraviolet wavelengths. Imaging system.
機械学習アルゴリズムを使用すること、 using machine learning algorithms;
前記生体サンプルの画像のうち1または複数を、1または複数の対応するカラー画像に変換すること、 converting one or more of the images of the biological sample into one or more corresponding color images;
前記生体サンプル内の細胞の固有なタイプの特定および/または表現型判定を行うこと、 identifying and/or phenotyping unique types of cells within the biological sample;
前記生体サンプル内の血液、骨髄、および/または組織の表現型判定を行うこと、 phenotyping blood, bone marrow, and/or tissue within the biological sample;
の1または複数を含む、請求項1から11のいずれか1つに記載の深紫外線イメージングシステム。 12. A deep ultraviolet imaging system according to any one of claims 1 to 11, comprising one or more of:
前記生体サンプルの画像のうち1または複数を、1または複数の対応するカラー画像に変換することと、 converting one or more of the images of the biological sample into one or more corresponding color images;
前記生体サンプルの画像のうち1または複数を1または複数の対応するカラー画像に変換する前に、前記紫外線波長のうち1または複数で撮像された空白の画像を用いて前記画像のうち1または複数を正規化すること、 one or more of the images of the biological sample with a blank image taken at one or more of the ultraviolet wavelengths before converting the one or more of the images into one or more corresponding color images; to normalize,
前記生体サンプルの画像のうち1または複数を1または複数の対応するカラー画像に変換する前に、前記画像のうち1または複数を、前記紫外線波長のうち1または複数に基づいて選ばれる重み係数およびガンマ係数を用いてスケーリングすること、 Prior to converting one or more of the images of the biological sample into one or more corresponding color images, one or more of the images are subjected to weighting factors selected based on one or more of the ultraviolet wavelengths and scaling using a gamma factor;
のうち1または複数と、を含む、請求項1から11のいずれか1つに記載の深紫外線イメージングシステム。 A deep ultraviolet imaging system according to any one of claims 1 to 11, comprising one or more of:
前記画像のうち1または複数を、前記紫外線波長のうち1または複数に基づいてRGB色空間のチャネルに割り当てることと、 assigning one or more of the images to channels in an RGB color space based on one or more of the ultraviolet wavelengths;
前記対応するカラー画像のうち1または複数の色が前記画像中の分子や構造体を区別するように前記変換を行うことと、 performing the conversion so that one or more colors of the corresponding color image distinguish molecules or structures in the image;
のうち1または複数を含む、請求項12または13に記載の深紫外線イメージングシステム。 14. A deep ultraviolet imaging system according to claim 12 or 13, comprising one or more of:
請求項1に記載の深紫外線イメージングシステムによって前記生体サンプルをイメージングすることを含む、方法。 A method for imaging a biological sample, the method comprising:
A method comprising imaging the biological sample with a deep ultraviolet imaging system according to claim 1.
前記紫外線波長のうち1または複数で前記生体サンプルの画像の1または複数を撮像することと、 capturing one or more images of the biological sample at one or more of the ultraviolet wavelengths;
前記生体サンプル内の1または複数の細胞の分類または特徴付けを行うために前記紫外線波長のうち1または複数で撮像された前記画像の1または複数を分析することと、を含む、請求項16に記載の生体サンプルをイメージングする方法。 and analyzing one or more of the images taken at one or more of the ultraviolet wavelengths to classify or characterize one or more cells within the biological sample. Methods for imaging the described biological samples.
生体サンプルの複数のマルチスペクトル紫外線画像を受信することであって、前記紫外線画像が、3つ以上の紫外線波長で撮像された画像を含む、受信することと、
各紫外線画像を、前記所与の紫外線画像に対応する波長で撮像された空白の画像を用いて正規化することと、
重み係数とガンマ係数を用いて各紫外線画像をスケーリングすることであって、所与の各紫外線画像の前記重み係数およびガンマ係数は、前記所与の紫外線画像の波長に基づいて選択される、スケーリングすることと、
スケーリングされた各紫外線画像を、前記スケーリングされた紫外線画像の波長に基づいてRGB色空間のチャネルに割り当てることは、第1の波長の紫外線画像を赤チャンネルに割り当て、第2の波長の紫外線画像を緑チャンネルに割り当て、第3の波長の紫外線画像を青チャンネルに割り当てることと、を含む、方法。 A method of processing an ultraviolet image of a biological sample, the method comprising:
receiving a plurality of multispectral ultraviolet images of a biological sample, the ultraviolet images including images taken at three or more ultraviolet wavelengths;
normalizing each UV image with a blank image taken at a wavelength corresponding to the given UV image;
scaling each ultraviolet image with a weighting factor and a gamma factor, the weighting factor and gamma factor for each given ultraviolet image being selected based on the wavelength of the given ultraviolet image; to do and
Assigning each scaled ultraviolet image to a channel of an RGB color space based on the wavelength of the scaled ultraviolet image includes assigning the ultraviolet image of a first wavelength to the red channel and assigning the ultraviolet image of a second wavelength to a channel of the RGB color space. and assigning a third wavelength ultraviolet image to the blue channel.
Applications Claiming Priority (5)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US201962913611P | 2019-10-10 | 2019-10-10 | |
US62/913,611 | 2019-10-10 | ||
US201962915495P | 2019-10-15 | 2019-10-15 | |
US62/915,495 | 2019-10-15 | ||
PCT/US2020/055431 WO2021072408A1 (en) | 2019-10-10 | 2020-10-13 | Label-free hematology and histopathology analysis using deep-ultraviolet microscopy |
Publications (2)
Publication Number | Publication Date |
---|---|
JP2022550916A JP2022550916A (en) | 2022-12-05 |
JPWO2021072408A5 true JPWO2021072408A5 (en) | 2023-10-16 |
Family
ID=75437456
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
JP2022521445A Pending JP2022550916A (en) | 2019-10-10 | 2020-10-13 | Label-free hematological and histopathological analysis using deep UV microscopy |
Country Status (4)
Country | Link |
---|---|
US (1) | US20220366709A1 (en) |
EP (1) | EP4042224A4 (en) |
JP (1) | JP2022550916A (en) |
WO (1) | WO2021072408A1 (en) |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11555994B2 (en) * | 2020-05-22 | 2023-01-17 | The Texas A&M University System | Wide-field deep UV Raman microscope |
Family Cites Families (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6198532B1 (en) * | 1991-02-22 | 2001-03-06 | Applied Spectral Imaging Ltd. | Spectral bio-imaging of the eye |
US5991028A (en) * | 1991-02-22 | 1999-11-23 | Applied Spectral Imaging Ltd. | Spectral bio-imaging methods for cell classification |
IL165329A0 (en) * | 2002-06-14 | 2006-01-15 | Pfizer | Metabolic phenotyping |
IL162617A (en) * | 2004-06-17 | 2010-04-15 | Nova Measuring Instr Ltd | Reflective optical system |
US8143600B2 (en) * | 2008-02-18 | 2012-03-27 | Visiongate, Inc. | 3D imaging of live cells with ultraviolet radiation |
EP2370951B1 (en) | 2008-11-27 | 2016-10-05 | Koninklijke Philips N.V. | Generation of a multicolour image of an unstained biological specimen |
EP2270712A1 (en) | 2009-06-30 | 2011-01-05 | Koninklijke Philips Electronics N.V. | Quality detection method and device for cell and tissue samples |
US10816550B2 (en) * | 2012-10-15 | 2020-10-27 | Nanocellect Biomedical, Inc. | Systems, apparatus, and methods for sorting particles |
US9836839B2 (en) * | 2015-05-28 | 2017-12-05 | Tokitae Llc | Image analysis systems and related methods |
-
2020
- 2020-10-13 EP EP20874587.7A patent/EP4042224A4/en active Pending
- 2020-10-13 JP JP2022521445A patent/JP2022550916A/en active Pending
- 2020-10-13 US US17/767,328 patent/US20220366709A1/en active Pending
- 2020-10-13 WO PCT/US2020/055431 patent/WO2021072408A1/en unknown
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