JP2008520317A - 医療画像データ内の腫瘍境界を自動的に検出及び区分するシステム及び方法 - Google Patents
医療画像データ内の腫瘍境界を自動的に検出及び区分するシステム及び方法 Download PDFInfo
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- JP2008520317A JP2008520317A JP2007542437A JP2007542437A JP2008520317A JP 2008520317 A JP2008520317 A JP 2008520317A JP 2007542437 A JP2007542437 A JP 2007542437A JP 2007542437 A JP2007542437 A JP 2007542437A JP 2008520317 A JP2008520317 A JP 2008520317A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/28—Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/12—Edge-based segmentation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/772—Determining representative reference patterns, e.g. averaging or distorting patterns; Generating dictionaries
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10132—Ultrasound image
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30068—Mammography; Breast
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30096—Tumor; Lesion
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Abstract
Description
距離=Sum(各軸の長さ)
を計算する。ここで、
M=Sum[距離(オリジナル)−距離(各候補)]
である。1024の候補区分の各々が、他の派生1024区分を第2ラウンドの摂動の後に出すので、候補区分の各々は、
N1=Sum[距離(候補1)−距離(各派生情報)]
N2=Sum[距離(候補2)−距離(各派生情報)]
…
N1024=Sum[距離(候補1024)−距離(各派生情報)]
である。
Claims (6)
- 医療画像内の部位を分類する方法であって、
分類部を医療画像トレーニングデータのセットでトレーニングするステップであり、該トレーニングデータが区分された部位を含み、該区分された部位を分類する臨床的なグランドトゥルースが知られているステップと、
検査のための非トレーニング医療画像データを得るステップと、
コンピュータ支援検出CADに対する処理を使用して、形態的に関心のある部位を識別及び区分するために前記トレーニングデータを処理するステップと、
前記区分された部位の各々に対する全ての特徴のセットを抽出するために前記区分された部位を処理するステップと、
前記特徴のサブセットを用いて関心のある前記部位を分類するステップと、
を有し、
前記トレーニングするステップが、安定した区分を実現するための推奨部を使用するステップを含む、方法。 - 前記トレーニングするステップが、更に、
前記部位の初期の区分又は境界で開始するステップと、
複数の候補の区分を実現するために、前記初期の区分を規定するパラメータの摂動を自動的に生成するステップと、
前記推奨部が、前記候補の区分が前記初期の区分よりも良いと決定するならば、前記候補の区分の1つを推奨するステップと、
を含む、請求項1に記載の方法。 - オペレーションのステップが、更に、第2パラメータ調整処理を含み、
ここで派生区分が、前記初期の及び候補の区分に対して生成される、
請求項2に記載の方法。 - 医療画像における興味のある特定の部位を区分又は図示する区分部であって、前記部位の候補の区分を生成し、前記区分部が、前記候補の区分の境界を変化又は摂動することによって、複数の区分を生成する推奨部を含み、もし前記区分部が、推奨された区分が後の区分処理に良く適合すると決定するならば、前記区分された正確性を改善するために前記摂動に基づいて、臨床の前記候補の部位に対して変化を推奨する、区分部。
- 前記推奨部が各派生情報で動作し、より所望の区分であるかどうかを決定するように各々を摂動する、請求項4に記載の区分部。
- CADシステム及び請求項4に記載の区分部と、
前記CADシステム及び区分部をつなぐ偽陽性削減システムであって、
特徴抽出部と、
前記特徴抽出部により抽出され、供給される最適特徴サブセットを生成する、前記特徴抽出部につながる生成アルゴリズムと、
前記特徴のサブセットに従って、最小の偽陽性で各区分された部位を分類する、特徴抽出部とつながるサポートベクトルマシンSVMと、
を有する偽陽性削減システムと、
を有する分類部。
Applications Claiming Priority (5)
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US62975404P | 2004-11-19 | 2004-11-19 | |
US60/629,754 | 2004-11-19 | ||
US72266705P | 2005-09-30 | 2005-09-30 | |
US60/722,667 | 2005-09-30 | ||
PCT/IB2005/053822 WO2006054267A1 (en) | 2004-11-19 | 2005-11-18 | System and method for automated detection and segmentation of tumor boundaries within medical imaging data |
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JP2008520317A true JP2008520317A (ja) | 2008-06-19 |
JP4949264B2 JP4949264B2 (ja) | 2012-06-06 |
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US (1) | US8265355B2 (ja) |
EP (1) | EP1815430A1 (ja) |
JP (1) | JP4949264B2 (ja) |
CN (1) | CN101061509B (ja) |
WO (1) | WO2006054267A1 (ja) |
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Cited By (7)
Publication number | Priority date | Publication date | Assignee | Title |
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US10154826B2 (en) | 2013-07-17 | 2018-12-18 | Tissue Differentiation Intelligence, Llc | Device and method for identifying anatomical structures |
US10716536B2 (en) | 2013-07-17 | 2020-07-21 | Tissue Differentiation Intelligence, Llc | Identifying anatomical structures |
JP2017524404A (ja) * | 2014-06-12 | 2017-08-31 | コーニンクレッカ フィリップス エヌ ヴェKoninklijke Philips N.V. | 画像をセグメント化するためのパラメータの最適化 |
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US11986341B1 (en) | 2016-05-26 | 2024-05-21 | Tissue Differentiation Intelligence, Llc | Methods for accessing spinal column using B-mode imaging to determine a trajectory without penetrating the the patient's anatomy |
US11701086B1 (en) | 2016-06-21 | 2023-07-18 | Tissue Differentiation Intelligence, Llc | Methods and systems for improved nerve detection |
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WO2006054267A1 (en) | 2006-05-26 |
EP1815430A1 (en) | 2007-08-08 |
US20090148007A1 (en) | 2009-06-11 |
US8265355B2 (en) | 2012-09-11 |
JP4949264B2 (ja) | 2012-06-06 |
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