JP6888770B2 - 試料内のアーチファクトを分類する方法と装置 - Google Patents
試料内のアーチファクトを分類する方法と装置 Download PDFInfo
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Description
102 試料容器
104 ラック
106、108、110 分析装置
121 トラック
122 キャリア
122H ホルダ
130 品質チェックモジュール
143 コンピュータ
212 試料
212SB 沈降した血液部分
212SP 血清又は血漿部分
212T 管
214 キャップ
215 識別情報
218 ラベル
235 凝塊
336 気泡
438 泡
540A〜540C カメラ
615、816 マルチクラス分類器
Claims (19)
- 試料容器内に収容された試料内のアーチファクトを判定する方法であって、
試料容器に分離され収容された試料を提供し、
複数の異なる露光及び複数の異なる波長で前記試料の画像をキャプチャし、
各波長における最適に露光された画像を生成するために、各波長における前記異なる露光で前記キャプチャされた画像から最適に露光された画素を選択し、
統計データを生成するために前記異なる波長で前記最適に露光された画素の統計を計算し、
前記統計データに基づいて前記試料の血清又は血漿部分を識別し、
前記統計データに基づいてアーチファクトが前記血清又は血漿部分の1つ以上の領域内に存在するか、又は前記血清又は血漿部分には存在しないかを分類し、
前記アーチファクトは、凝塊、気泡及び泡を含む群の中から識別されることを特徴とする、
方法。 - 前記試料は遠心分離され、分離された血液部分と血清又は血漿部分とを有する請求項1に記載の方法。
- 前記試料の画像をキャプチャすることは、複数の視点から撮影された複数の画像をキャプチャすることを含む請求項1又は2に記載の方法。
- 前記複数の視点の数は3以上である請求項3に記載の方法。
- 前記試料の提供は、前記試料を収容する前記試料容器を保持部に固定することを含む請求項1〜4のいずれか1つに記載の方法。
- 前記複数の異なる波長は、約400nmから約700nmの間の少なくとも2つの波長を含む請求項1〜5のいずれか1つに記載の方法。
- 前記複数の波長は、約455nm、約537nm、及び約634nmの群から選択される少なくとも2つの波長を含む請求項1〜6のいずれか1つに記載の方法。
- 前記複数の露光の時間は、約0.1msから約256msの間を含む請求項1〜7のいずれか1つに記載の方法。
- 最適に露光された画素を選択することは、0から255の強度範囲に基づいて約180から254の間の強度を含む画素を前記画像から選択することを含む請求項1〜8のいずれか1つに記載の方法。
- 血清又は血漿部分を識別することは、複数のトレーニングセットから生成されたマルチクラス分類器に基づいて、最適に露光された画像データの画素を分類することに基づく請求項1〜9のいずれか1つに記載の方法。
- 前記マルチクラス分類器は、サポートベクトルマシン又はランダム判定ツリーをさらに含む請求項10に記載の方法。
- 前記統計データに基づいて、アーチファクトが前記血清又は血漿部分の1つ以上の領域内に存在するか、又は前記血清又は血漿部分に存在しないかを分類することは、複数のアーチファクトトレーニングセットから生成された1つ以上の分類子に基づいている請求項1〜11のいずれか1つに記載の方法。
- 前記1つ以上の分類子は、凝塊、気泡及び泡それぞれのための別々の2進分類子を有する請求項12に記載の方法。
- 各波長についての前記最適に露光された画像データから前記最適に露光された画素の統計を計算することは、それぞれの波長からの対応する画素の集合から平均値、標準偏差、又は共分散のうちの少なくとも1つを計算することを含む請求項1〜13のいずれか1つに記載の方法。
- 前記キャプチャした画像内のバーコードを解読することに基づいて前記試料の固有性を判定することを含む請求項1〜14のいずれか1つに記載の方法。
- 3Dモデルを形成するために、前記試料の前記最適に露光された画像データを処理してセグメント化を行うことを含む請求項1〜15のいずれか1つに記載の方法。
- 前記分離された試料の血清又は血漿部分を識別することは、前記画像内の保持部の一部
分を無視する請求項1〜16のいずれか1つに記載の方法。 - 試料容器内に収容された試料中のアーチファクトの存在を判定するように構成された品質チェックモジュールであって、
前記試料容器の周りに配置され、複数の視点から前記試料容器の複数の画像をキャプチャするように構成され、複数の異なる露光時間及び複数の異なる波長で撮影された複数の画像を生成するようにそれぞれ構成された複数のカメラと、
前記複数のカメラに接続され、前記画像から画像データを処理するように構成されたコンピュータであって、
それぞれの波長において最適に露光された画像データを生成するために、前記異なる露光時間で前記画像から最適に露光された画素を選択し、
統計データを生成するために、それぞれの前記波長で前記最適に露光された画素の統計を計算し、
前記試料の血清又は血漿部分を識別し、
前記統計データに基づいて、アーチファクトが血清又は血漿部分の1つ以上の領域内に存在するか、又は血清又は血漿部分に存在しないかを分類するように構成され操作可能な前記コンピュータと、
を有し、
前記アーチファクトは、凝塊、気泡及び泡を含む群の中から識別されることを特徴とする、
品質チェックモジュール。 - 試料容器内に収容された試料中のアーチファクトの存在を判定するように構成された検査装置であって、
トラックと、
前記試料容器を収容するように構成された前記トラック上のキャリアと、
前記トラックの周囲に配置され、複数の視点から前記試料容器の複数の画像をキャプチャするように構成され、複数の異なる露光及び複数の異なる波長で複数の画像を生成するように構成された複数のカメラと、
前記カメラに接続され、前記複数の画像から画像データを処理するように構成されたコンピュータであって、
各波長に対して最適に露光された画像データを生成するために、前記異なる露光で前記複数の画像から最適に露光された画素を選択し、
統計データを生成するために前記異なる波長で前記最適に露光された画素の統計を計算し、
前記試料の血清又は血漿部分を識別し、
前記統計データに基づいて、アーチファクトが前記血清又は血漿部分の1つ以上の領域内に存在するか、又は前記血清又は血漿部分には存在しないか、を分類するように構成され操作可能なコンピュータと、
を有し、
前記アーチファクトは、凝塊、気泡及び泡を含む群の中から識別されることを特徴とする、
する検査装置。
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