发明名称 |
POSITIONING ANATOMICAL LANDMARKS IN VOLUME DATA SETS |
摘要 |
Certain embodiments provide a computer apparatus operable to carry out a data processing method to position a set of anatomical landmarks in a three-dimensional image data set of a part or all of a patient, comprising: providing a trained supervised machine learning algorithm which has been trained to place each of the set of anatomical landmarks; applying the supervised machine learning algorithm to place the set of anatomical landmarks relative to the data set; providing a trained point distribution model, including a mean shape and a covariance matrix, wherein the mean shape represents locations of the set of landmarks in a variety of patients; and applying the point distribution model to the set of landmarks with the locations output from the supervised machine learning algorithm by: removing any landmarks whose locations have an uncertainty above a threshold with reference to the mean shape and covariance matrix; followed by: an optimisation of the locations of the remaining landmarks by maximising joint likelihood that a new shape, derived from linear combinations of eigenvectors of the covariance matrix, is plausible. |
申请公布号 |
US2014314290(A1) |
申请公布日期 |
2014.10.23 |
申请号 |
US201313867560 |
申请日期 |
2013.04.22 |
申请人 |
KABUSHIKI KAISHA TOSHIBA ;TOSHIBA MEDICAL SYSTEMS CORPORATION |
发明人 |
DABBAH Mohammad;Poole Ian |
分类号 |
G06T7/00;G06K9/00 |
主分类号 |
G06T7/00 |
代理机构 |
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代理人 |
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主权项 |
1. A computer apparatus operable to carry out a data processing method to position a set of anatomical landmarks in a three-dimensional image data set of a part or all of a patient, comprising:
a. providing a trained supervised machine learning algorithm which has been trained to place each of the set of anatomical landmarks; b. applying the supervised machine learning algorithm to place the set of anatomical landmarks relative to the data set; c. providing a trained point distribution model, including a mean shape and a covariance matrix, wherein the mean shape represents locations of the set of landmarks in a variety of patients; and d. applying the point distribution model to the set of landmarks with the locations output from the supervised machine learning algorithm by:
(i) removing any landmarks whose locations have an uncertainty above a threshold with reference to the mean shape and covariance matrix; followed by:(ii) an optimisation of the locations of the remaining landmarks by maximising joint likelihood that a new shape, derived from linear combinations of eigenvectors of the covariance matrix, is plausible. |
地址 |
Tokyo JP |