JP7258107B2 - 3d画像化スピン中に撮影された2d画像を用いた自動セグメント化 - Google Patents
3d画像化スピン中に撮影された2d画像を用いた自動セグメント化 Download PDFInfo
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Description
2)85%L1、76%L2、75%L3、75%L4、69%L5
3)86%L1、75%L2、76%L3、75%L4、70%L5
4)85%L1、74%L2、75%L3、75%L4、70%L5
5)84%L1、75%L2、75%L3、74%L4、71%L5
しかし、さらに、次のレベルの確実性スコアが以下のようになっていると仮定する。
2)83%L1、80%L2、85%L3、82%L4、76%L5
3)80%L1、88%L2、83%L3、84%L4、76%L5
4)84%L1、74%L2、84%L3、82%L4、76%L5
5)83%L1、88%L2、78%L3、82%L4、77%L5
両方のデータセットで、5つの値の平均は同じになる。しかしながら、第2のデータセットでは、L1は第1のショットで競合する名前から大きく乖離していた(95%L1対45%L2またはその他=確実性の差50%)が、第1のケースでは、確実性の乖離は小さい(レベル名がL1対他の名前で確実性の差9%~11%)。したがって、本方法では、次の下のレベルが第1のレベルよりもL1である可能性が高いと判断することができる。レベルには順番に番号が付けられていることが分かっているため、L1がL2より上、L3より上など、本方法では、最初に全てのレベルを考慮し、最も可能性の高い全体的なレベルを検出してから、幾何学的に該当するところに従って、他のレベルに名前を付ける。
Claims (15)
- コーンビームCT画像から解剖学的構造を識別およびセグメント化する方法であって、 コーンビームCTデバイスから、患者の360度スキャンから撮影された複数のX線画像の一部である、少なくとも1つのX線画像を受信することであって、前記少なくとも1つのX線画像は、少なくとも1つの解剖学的構造を含む、受信することと、
解剖学的構造の保存されたモデルに基づいて、前記X線画像に含まれる前記少なくとも1つの解剖学的構造を識別およびセグメント化することと、
前記360度スキャンからの複数のX線画像から3D画像ボリュームを作成することと、
前記少なくとも1つのX線画像から引き出された識別およびセグメント化情報を、前記作成された3D画像ボリュームに追加することを含み、
前記識別およびセグメント化するステップは、前記X線画像に含まれる少なくとも1つの椎体を識別およびセグメント化することを含み、
前記受信することは、規則的に離間した角度配向で一連のX線画像を受信することを含み、
各受信したX線画像に対して、
前記各X線画像における解剖学的構造の識別およびセグメント化についての信頼レベルを判定することと、
前記信頼レベルに基づいて、最適な識別およびセグメント化情報を判定することと、を含み、
前記セグメント化するステップが、少なくとも1つの解剖学的構造の中心を判定すること、を含む、方法。 - 前記最適な識別およびセグメント化情報を判定するステップが、信頼レベルに基づいて、前記X線画像に重み付けすることによって前記解剖学的構造の前記中心を判定することを含む、請求項1に記載の方法。
- 前記最適な識別およびセグメント化情報を判定するステップが、所定の信頼レベルよりも低い信頼レベルを有する前記X線画像を除外することを含む、請求項2に記載の方法。
- 前記受信するステップが、前記360度スキャンから、Nが5以上に等しいN番目ごとのX線画像を受信することを含む、請求項1に記載の方法。
- 前記各N番目のX線画像を受信するステップが、前記各N番目のX線画像の角度配向情報を受信することを含む、請求項4に記載の方法。
- 前記信頼レベルを判定するステップが、前記少なくとも1つの解剖学的構造の前記中心の判定についての信頼レベルを判定することを含む、請求項1に記載の方法。
- コーンビームCT画像から椎体を識別およびセグメント化する方法であって、
コーンビームCTデバイスから、異なる角度配向で撮影された一連のX線画像を受信することであって、各画像が少なくとも1つの椎体を含む、受信することと、
各受信したX線画像に対して、
椎体の保存されたモデルに基づいて、前記X線画像に含まれる少なくとも1つの椎体を識別およびセグメント化することと、
各X線画像の識別およびセグメント化の信頼レベルを判定することと、
前記信頼レベルに基づいて、最適な識別およびセグメント化情報を判定することと、を含み、
360度スキャンからの複数のX線画像から3D画像ボリュームを作成することであって、前記複数のX線画像が、前記一連のX線画像を含む、作成することと、
前記作成された3D画像ボリュームに前記最適な識別およびセグメント化情報を追加することと、および
前記最適な識別およびセグメント化情報を判定するステップが、前記信頼レベルに基づいて、前記X線画像に重み付けすることによって前記椎体の中心を判定することを含む、方法。 - 前記識別およびセグメント化するステップが、前記作成するステップの前に行われる、請求項7に記載の方法。
- 前記最適な識別およびセグメント化情報を判定するステップが、所定の信頼レベルよりも低い信頼レベルを有する前記X線画像を除外することを含む、請求項7に記載の方法。
- 前記受信するステップが、前記360度スキャンから、Nが5以上に等しいN番目ごとのX線画像を受信することを含む、請求項7に記載の方法。
- 前記各N番目のX線画像を受信するステップが、前記各N番目のX線画像の角度配向情報を受信することを含む、請求項10に記載の方法。
- 前記セグメント化するステップが、前記少なくとも1つの椎体の中心を判定することを含む、請求項7に記載の方法。
- 前記信頼レベルを判定するステップが、前記少なくとも1つの椎体の前記中心の判定についての信頼レベルを判定することを含む、請求項12に記載の方法。
- 前記360度スキャンからの複数のX線画像から3D画像ボリュームを作成することであって、前記複数のX線画像が、前記一連のX線画像を含む、作成することと、
前記作成された3D画像ボリュームに前記最適な識別およびセグメント化情報を追加することと、
ユーザが操作できるように、前記3D画像ボリュームを前記追加された識別およびセグメント化情報と共に表示することと、をさらに含む、請求項8に記載の方法。 - 表示された3D画像ボリュームを操作するためのユーザ入力を受信することと、
前記受信されたユーザ入力に応答して、前記表示された3D画像ボリュームを三次元で操作することと、をさらに含む、請求項14に記載の方法。
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