JP6653706B2 - 血管腔サブ解像度セグメンテーション - Google Patents
血管腔サブ解像度セグメンテーション Download PDFInfo
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Description
Claims (12)
- 血管腔の狭窄を検出して前記血管腔をセグメント化する撮像システムであって、
1つ以上のプロセッサであり、
画像データを受信し、
前記画像データから、前記血管腔を通る中心線を生成し、
前記画像データ及び前記中心線を近似することによって可視の管腔径を推定し、
中心線プロファイル分析を用いて、当該撮像システムの可視的な解像度よりも高い精細さで、前記画像データ内の前記血管腔の前記狭窄を検出し、前記中心線プロファイル分析は、所定の最小閾値よりも低い又は所定の最大閾値よりも高い強度プロファイルに基づいて、前記血管腔の前記狭窄を検出し、前記強度プロファイルは、前記中心線に沿った複数の断面内のボクセルをサンプリングすることによって取得され、
前記推定した可視の管腔径を、検出された前記血管腔の前記狭窄を用いて修正することによって、前記中心線に沿った前記複数の断面におけるサブ解像度の直径を計算する、
ように構成された1つ以上のプロセッサ、
を有する撮像システム。 - 前記1つ以上のプロセッサは更に、前記画像データの前記血管腔を、前記血管腔を表す第1の部分と、背景を表す第2の部分とにセグメント化するように構成され、前記第1の部分内の各ボクセルに第1及び第2の値が割り当てられ、前記第1の値は、訓練されたモデルによって使用される最も近い中心線ボクセルの強度に基づいて、そのボクセルが前記第1の部分に含まれる可能性がどの程度であるかを指し示し、前記第2の値は、最も近い中心線ポイントからのそのボクセルの空間的距離及び前記サブ解像度の直径に基づいて、そのボクセルが前記第1の部分に含まれる可能性がどの程度であるかを指し示す、請求項1に記載の撮像システム。
- 前記サブ解像度の直径は、前記血管腔の前記中心線に沿った各断面について前記強度プロファイルの半値全幅強度を用いて計算される直径に基づいて計算される、請求項1に記載の撮像システム。
- 前記訓練されたモデルは、前記中心線に沿った前記複数の断面内の前記ボクセルの強度の特性をモデル化する、請求項2に記載の撮像システム。
- 前記訓練されたモデルは、前記中心線に沿った前記複数の断面内の前記ボクセルの強度の最小閾値にフィッティングされた線形関数を含む、請求項4に記載の撮像システム。
- 前記訓練されたモデルは、前記中心線に沿った前記複数の断面内の前記ボクセルの強度の最大閾値にフィッティングされた線形関数を含む、請求項4に記載の撮像システム。
- 撮像システムが血管腔の狭窄を検出して前記血管腔をセグメント化する方法であって、
画像データを受信し、
前記画像データから、前記血管腔を通る中心線を生成し、
前記画像データ及び前記中心線を近似することによって可視の管腔径を推定し、
中心線プロファイル分析を用いて、前記撮像システムの可視的な解像度よりも高い精細さで、前記画像データ内の前記血管腔の前記狭窄を検出し、前記中心線プロファイル分析は、所定の最小閾値よりも低い又は所定の最大閾値よりも高い強度プロファイルに基づいて、前記血管腔の前記狭窄を検出し、前記強度プロファイルは、前記中心線に沿った複数の断面内のボクセルをサンプリングすることによって取得され、
前記推定した可視の管腔径を、検出された前記血管腔の前記狭窄を用いて修正することによって、前記中心線に沿った前記複数の断面におけるサブ解像度の直径を計算する、
ことを有する方法。 - 当該方法は更に、前記画像データの前記血管腔を、前記血管腔を表す第1の部分と、背景を表す第2の部分とにセグメント化することを有し、前記第1の部分内の各ボクセルに第1及び第2の値が割り当てられ、前記第1の値は、訓練されたモデルによって使用される最も近い中心線ボクセルの強度に基づいて、そのボクセルが前記第1の部分に含まれる可能性がどの程度であるかを指し示し、前記第2の値は、最も近い中心線ポイントからのそのボクセルの空間的距離及び前記サブ解像度の直径に基づいて、そのボクセルが前記第1の部分に含まれる可能性がどの程度であるかを指し示す、請求項7に記載の方法。
- 前記サブ解像度の直径は、前記血管腔の前記中心線に沿った各断面について前記強度プロファイルの半値全幅強度を用いて計算される直径に基づいて計算される、請求項7に記載の方法。
- 前記訓練されたモデルは、前記中心線に沿った前記複数の断面内の前記ボクセルの強度の特性をモデル化する、請求項8に記載の方法。
- 前記訓練されたモデルは、前記中心線に沿った前記複数の断面内の前記ボクセルの強度の最小閾値にフィッティングされた線形関数を含む、請求項10に記載の方法。
- 前記訓練されたモデルは、前記中心線に沿った前記複数の断面内の前記ボクセルの強度の最大閾値にフィッティングされた線形関数を含む、請求項10に記載の方法。
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PCT/IB2016/050156 WO2016113690A1 (en) | 2015-01-16 | 2016-01-14 | Vessel lumen sub-resolution segmentation |
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EP (1) | EP3245632B1 (ja) |
JP (1) | JP6653706B2 (ja) |
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EP4390845A1 (en) | 2022-12-22 | 2024-06-26 | Hemolens Diagnostics Spólka Z Ograniczona Odpowiedzialnoscia | A computer-implemented method, computer program product and imaging system |
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