JP2020512129A - オブジェクトグリッド増強を用いて高次元画像データから低次元画像データを合成するためのシステムおよび方法 - Google Patents
オブジェクトグリッド増強を用いて高次元画像データから低次元画像データを合成するためのシステムおよび方法 Download PDFInfo
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Abstract
Description
Claims (23)
- ***組織画像データを処理するための方法であって、
患者の***組織の画像データを処理し、前記患者の***組織内の1つ以上の高次元オブジェクトを描写する高次元グリッドを生成することと、
前記高次元グリッドに描写される前記1つ以上の高次元オブジェクトのそれぞれの信頼度または確率を決定することと、
前記1つ以上の高次元オブジェクトのより低い次元のフォーマットバージョンを生成することと
を含む、方法。 - 前記患者の***組織の画像データを処理することは、前記患者の***組織を集合的に描写する画像スライスのセットを生成することを含み、前記高次元グリッドは、少なくとも部分的に、前記画像スライスのセットに基づいて生成される、請求項1に記載の方法。
- 前記1つ以上の高次元オブジェクトのより低い次元のフォーマットバージョンを生成することは、少なくとも部分的に、その個別の決定された信頼度または確率に基づいて、前記1つ以上の高次元オブジェクトのうちの少なくとも1つの1つ以上の側面を修正することを含む、請求項1または2に記載の方法。
- 前記1つ以上の高次元オブジェクトのより低い次元のフォーマットバージョンは、少なくとも部分的に、オブジェクト内組み合わせに基づく、請求項1−3のいずれかに記載の方法。
- 前記1つ以上の高次元オブジェクトのより低い次元のフォーマットバージョンは、少なくとも部分的に、オブジェクト間組み合わせに基づく、請求項1−3のいずれかに記載の方法。
- 前記1つ以上の高次元オブジェクトのより低い次元のフォーマットバージョンは、少なくとも部分的に、オブジェクトと背景を組み合わせることに基づく、請求項1−3のいずれかに記載の方法。
- 前記1つ以上の高次元オブジェクトのより低い次元のフォーマットバージョンを前記患者の***組織の合成された2次元画像内に表示することをさらに含む、請求項1−6のいずれかに記載の方法。
- 高次元オブジェクトグリッドは、正常および/または異常***組織構造を描写する1つ以上のオブジェクトを備える、請求項1−7のいずれかに記載の方法。
- 前記1つ以上のオブジェクトは、***実質組織構造全体を集合的に表す複数のオブジェクトを備える、請求項8に記載の方法。
- 前記1つ以上のオブジェクトはそれぞれ、個別の属性のセットと関連付けられ、各属性は、個別のオブジェクトによって描写される前記***組織構造の特性を表す、請求項8に記載の方法。
- 前記属性のセットは、前記オブジェクトによって描写される個別の***組織構造の場所、サイズ、形状、および形態のうちの1つ以上のものを集合的に表す、請求項10に記載の方法。
- 前記1つ以上のオブジェクトは、第1のオブジェクトタイプに対応する1つ以上のオブジェクトと、第2のオブジェクトタイプに対応する1つ以上のオブジェクトとを含む、請求項8に記載の方法。
- 前記第1のオブジェクトタイプは、微細石灰化および塊状物を含む異常***病変に対応し、前記第2のオブジェクトタイプは、乳首、胸筋筋肉、および***実質組織を含む正常***構造に対応する、請求項12に記載の方法。
- 前記第1および第2のオブジェクトタイプのそれぞれのためのパターン認識方法を使用して、一方または両方のオブジェクトタイプが、前記患者の***組織を描写する前記画像データ内に存在するかどうかを決定することをさらに含む、請求項12または13に記載の方法。
- 前記パターン認識方法は、1つ以上の機械学習アルゴリズムを利用する、請求項14に記載の方法。
- 前記高次元グリッド内の前記1つ以上の高次元オブジェクトのそれぞれの確率または信頼度は、少なくとも部分的に、前記1つ以上の高次元オブジェクトのそれぞれに割り当てられた加重に基づいて決定され、前記第1のオブジェクトタイプのオブジェクトに割り当てられた加重は、前記第2のオブジェクトタイプのオブジェクトに割り当てられた加重を上回る、請求項13−15のいずれかに記載の方法。
- 個別の1つ以上の高次元オブジェクトのより低い次元のフォーマットバージョンを生成することは、前記第1のオブジェクトタイプに対応するオブジェクトを修正し、それによって、前記第2のオブジェクトタイプに対応するオブジェクトより強調することを含む、請求項16に記載の方法。
- 前記1つ以上の高次元オブジェクトが前記表示される合成された画像上で重複する可能性が高いかどうかを決定することをさらに含む、請求項16または17に記載の方法。
- 前記1つ以上の高次元オブジェクトが前記合成された画像上で重複する可能性が高いことが決定される場合、前記重複する高次元オブジェクトの1つ以上の最も臨床上有意な特徴が表示されるように、前記重複する高次元オブジェクトの特徴を修正することをさらに含む、請求項18に記載の方法。
- 前記1つ以上の高次元オブジェクトが前記合成された画像上で重複する可能性が高いことが決定される場合、最高加重を割り当てられた前記高次元オブジェクトのみを表示することをさらに含む、請求項18に記載の方法。
- 前記1つ以上の高次元オブジェクトが重複する可能性が高いことが決定される場合、
重複する可能性が高い前記1つ以上の高次元オブジェクトのそれぞれの割り当てられた加重の差異を決定することと、
前記決定された差異が、閾値より低い場合、重複する可能性が高い前記1つ以上の高次元オブジェクトを表示することであって、前記1つ以上の高次元オブジェクトの特徴の修正は、より高い加重を割り当てられた前記高次元オブジェクトの少なくとも一部をより低い加重を割り当てられた前記高次元オブジェクトに対して強調することに関する、ことと
をさらに含む、請求項18に記載の方法。 - 前記高次元データは、3次元データと等しいまたはより高い次元を備える、請求項1−21のいずれかに記載の方法。
- 前記高次元データは、3次元トモシンセシス再構成スライスデータを備え、前記より低い次元のフォーマットバージョンは、2次元の合成された画像データを備える、請求項22に記載の方法。
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WO2018183549A1 (en) | 2018-10-04 |
EP3600051B1 (en) | 2024-05-01 |
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US11983799B2 (en) | 2024-05-14 |
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