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WO2008065590A1 - Segmentation améliorée - Google Patents

Segmentation améliorée Download PDF

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Publication number
WO2008065590A1
WO2008065590A1 PCT/IB2007/054751 IB2007054751W WO2008065590A1 WO 2008065590 A1 WO2008065590 A1 WO 2008065590A1 IB 2007054751 W IB2007054751 W IB 2007054751W WO 2008065590 A1 WO2008065590 A1 WO 2008065590A1
Authority
WO
WIPO (PCT)
Prior art keywords
model
shape model
data set
patient
shape
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/IB2007/054751
Other languages
English (en)
Inventor
Cristian Lorenz
Hans Barschdorf
Jens Von Berg
Thomas Blaffert
Sebastian P. M Dries
Sven Kabus
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Philips Intellectual Property and Standards GmbH
Koninklijke Philips NV
Original Assignee
Philips Intellectual Property and Standards GmbH
Koninklijke Philips Electronics NV
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Philips Intellectual Property and Standards GmbH, Koninklijke Philips Electronics NV filed Critical Philips Intellectual Property and Standards GmbH
Publication of WO2008065590A1 publication Critical patent/WO2008065590A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/149—Segmentation; Edge detection involving deformable models, e.g. active contour models
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • 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
    • G06T2207/30048—Heart; Cardiac

Definitions

  • the invention relates to a computer program product to adapt a shape model to a patient data set, the data set representing at least a region of anatomy of the patient.
  • a shape model can be described as a data structure which represents a true anatomical shape in the body of a patient. It is a mathematical model which can be made to encode the physical, geometrical shape of an anatomical object, or a collection of anatomical objects.
  • This document discloses the setting up of a shape model from mean, i.e. average, patient data to produce a model based on a list of nodes and vertices in a model coordinate system which describe the shape of an average human heart.
  • the nodes and vertices define a collection of connected triangles which form a virtual mesh taking the shape, in virtual space, of the mean heart.
  • the model can be linked to a patient image data set representing or including the real heart of the patient and the model can be caused to adapt to the real heart in the data set using a method of alignment based on the maximization of two terms.
  • the mesh as disclosed above is constructed from triangles, but it is found that other geometrical shapes can be used to form a geometrical mesh.
  • shape models may be constructed from any other mathematical means which allow description of the geometrical shape of an anatomical object or objects and examples include for example models based on geometrical functions and models based on series expansions. Regardless of the mathematical manner in which the model is set up, it can be used to describe anatomical objects such as the heart, the brain, the liver, the internal structure of the heart including the heart chambers, and also collections of such objects.
  • the shape model when linked to a patient data set containing an example of the anatomical object which is the subject of the model and subsequently adapted according to the known process for that model, aligns itself to the shape of the real object as represented in the data set and thereby forms an acceptable segmentation of the real anatomical object.
  • the shape model is pre- adapted to a data set representing substantially the same anatomy of the same patient.
  • the shape model as a mathematical construct to be applied to a data set, can be usefully applied to any data set representing or containing an example of the anatomical structure represented by the shape model and can be adapted to fit that object using the known method.
  • the mean shape model as initially applied to each data set is simply a mathematical representation of an average form of the anatomical object
  • the resultant adapted model is also merely a mathematical representation of the object, this time the object as represented in the data set to which the mean model has already been applied.
  • the mathematical representation of the adapted model is in the same mathematical form as the mathematical representation of the mean shape model and can therefore also be applied to the contents of a data set.
  • the mathematical model is in the form of a list of points describing a series of nodes and vertices which together make up the triangles of the model mesh.
  • the known model as described in the prior art, is a mean model and as such describes the average geometrical shape of the anatomy which constitutes the subject of the model.
  • the adaptation of the mean model to data representing the patient introduces a congruency into the model which when applied to a data set for further adaptation produces a better and more consistent segmentation.
  • This pre-adaptation can be achieved by instigating an adaptation procedure using the known mean model according to the known method to produce the resulting adapted model.
  • This resulting adapted model now becomes the pre-adapted model for the next adaptation procedure for the particular anatomy under consideration and for that particular patient.
  • the pre-adapted model can be stored electronically prior to use, although this is not necessary for the invention to work.
  • a mean model will be adapted to the resultant image data to produce an adapted model and therefore to produce also medical results, as is known in the art, and the model will be stored for a period of time until the next similar or equivalent imaging session for the same patient at which a new similar data set is produced.
  • the adapted model will then be retrieved from data storage, according to known data handling methods, and applied as the new 'mean model' to the newly acquired data set for adaptation. This will result in an improved segmentation of the data in the data set.
  • the vertex positions of the triangular surface mesh are the parameters which are varied and it is the vertices V 1 fully describe an individual surface.
  • Mesh deformation is performed by minimizing the energy term
  • the external energy E ext drives the mesh towards the surface points obtained in a surface detection step.
  • the internal energy E mt restricts the flexibility by penalizing differences from the shape model.
  • the parameter ⁇ weights the influence of both terms. A fixed number n of such minimization steps is performed on the mesh. The different components of the deformation algorithm are described below.
  • C 1 argmax ⁇ ⁇ (X 1 +M 1 Cj -BIcJI 2 I M 1 is a rotational matrix that rotates the z-axis of the local co-ordinate system to the triangle surface normal Xi 1 and C
  • the sampling grid is the new surface point for X 1 .
  • the parameter ⁇ controls the trade-off between feature strength and distance.
  • the sampling grid is the new surface point for X 1 .
  • the parameter ⁇ controls the trade-off between feature strength and distance.
  • E INT ⁇ ⁇ ((V j -Vj -JR(V j -Vj) 2 ,
  • N(j) is the set of neighbors of vertex/ The neighboring vertices are those connected by a single triangle edge.
  • the scaling factor s and the rotational matrix R are determined by a closed- form point-based registration method based on a singular value decomposition prior to calculation of the internal energy. As only interdependences between neighbor vertices exist and the energy terms are of a quadratic form, the conjugate gradient method could be used for minimization of the energy term with a sparsely filled matrix.
  • the labels assigned to each face of the multi- surface model may be used to parameterize interfaces between different anatomical entities specifically.
  • a shape model in being representative of the shape of an anatomical object, also contains a representation of the constituent structures within that anatomical object and as such contains information relating to size, structure, relation in space etc.
  • an adapted model contains information pertaining to the specific structure of the relevant organ within an individual patient.
  • the adapted shape model therefore serves as a useful source of information for the patient record and also serves as a useful store of information for data analysis on the patient.
  • the information contained in the adapted shape models can be accessed by a suitably arranged data analysis program to allow comparison of information in the shape models.
  • change in left ventricular volume over a period in time can be calculated by comparing left ventricular volume from two adapted shape models, both adapted to the patient heart.
  • the invention therefore also relates to a computer program product for the comparison of quantitative patient data and arranged to access quantitative patient data contained in a patient data record, characterized in that the quantitative patient data is in the form of an adapted shape model, adapted from a pre-adapted shape model.
  • This has the further advantage that the comparison of shape model data can be improved by use of adapted shape models which were themselves adapted from pre-adapted shape models.
  • the invention also relates to a patient data record characterized in that it comprises at least one stored pre-adapted shape model.
  • a patient data record characterized in that it comprises at least one stored pre-adapted shape model.
  • Fig. 1 shows a flow diagram showing how the invention can be used in practice.
  • Fig. 2 shows a further advantageous embodiment of the invention.
  • Fig. 3 shows the use of a data comparator to compare results derived from pre- adapted shape models.
  • Figure 1 describes an embodiment in which the invention can be used.
  • a patient 101 presents for imaging at a first imaging session 102 as a result of which a first patient data set 103 is produced.
  • a mean shape model 104 conveniently stored in computer storage 105 is linked to the first patient data set 103 and following the known adaptation process 106 results in the shape model adapted to the data set 107.
  • the adapted shape model 108 is a mathematical model of the same form as the original mean model 104.
  • This first adapted shape model 108 can be stored in storage 109 which may or may not be the same as storage 105.
  • the first adapted shape model 108 can be linked to the second patient data set 111 and following adaptation process 112 will produce a shape model adapted to the data set 113.
  • This further adapted shape model 114 can be stored in storage 115 which may or may not be the same as storage 105 or 109.
  • the adaptation process 206 results in a shape model adapted to the data set 207 and an adapted shape model 208 which is now immediately re-linked to the original data set 209, equal to data set 203, to undergo a new adaptation process 210 to produce a new data set plus adapted model combination 211.
  • the adaptation process 210 produces an improved output shape model 212.
  • Figure 3 shows the use of a data comparator to compare results derived from pre-adapted shape models, in which a mean model 301 is linked to data sets 302 and 303 which both undergo the known adaption process 304 to produce respective pre-adapted shape models 305 and 306.
  • the pre-adapted shape models 305 and 306 are then linked to data sets 307 and 308 which then undergo the adaptation process 304.
  • Data set 307 may or may not be equal to data set 303.
  • Data set 308, may or may not be equal to data set 302.
  • the resulting shape models 309 and 310 both the result of having used pre-adapted shape models 305 and 306, are supplied as input to a data analysis package 311 and used for comparison 312 of the results and data information held within the respective shape models. It can therefore be seen that the use of pre-adapted shape models as input to the shape model adaptation process produces an improved shape model adaptation process.

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  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Software Systems (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

La présente invention concerne un programme informatique conçu pour adapter un modèle de forme à un ensemble de données patient, ledit ensemble de données représentant au moins une région anatomique du patient et le modèle de forme étant préadapté pour un ensemble de données représentant une anatomie sensiblement identique à celle du patient. Ceci permet d'obtenir une meilleure segmentation des données dans l'ensemble de données. L'invention porte aussi sur un programme informatique destiné à comparer les données quantitatives d'un patient et élaboré de manière à pouvoir accéder aux données quantitatives du patient contenues dans l'enregistrement des données du patient. Le programme informatique se caractérise en ce que les données quantitatives du patient se trouvent sous la forme d'un modèle de forme adapté, conçu à partir d'un modèle de forme préadapté. Ceci permet d'étendre l'amélioration obtenue par l'utilisation de modèles de forme préadaptés et de les étendre à l'utilisation de modèles de forme préadaptés en vue d'une comparaison de données.
PCT/IB2007/054751 2006-11-28 2007-11-22 Segmentation améliorée Ceased WO2008065590A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP06124949 2006-11-28
EP06124949.6 2006-11-28

Publications (1)

Publication Number Publication Date
WO2008065590A1 true WO2008065590A1 (fr) 2008-06-05

Family

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Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/IB2007/054751 Ceased WO2008065590A1 (fr) 2006-11-28 2007-11-22 Segmentation améliorée

Country Status (1)

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WO (1) WO2008065590A1 (fr)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050148852A1 (en) * 2003-12-08 2005-07-07 Martin Tank Method for producing result images for an examination object
WO2006085257A2 (fr) * 2005-02-10 2006-08-17 Koninklijke Philips Electronics N.V. Procede, systeme et programme informatique pour segmenter une surface dans un ensemble de donnees multidimensionnel

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050148852A1 (en) * 2003-12-08 2005-07-07 Martin Tank Method for producing result images for an examination object
WO2006085257A2 (fr) * 2005-02-10 2006-08-17 Koninklijke Philips Electronics N.V. Procede, systeme et programme informatique pour segmenter une surface dans un ensemble de donnees multidimensionnel

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
MIN-JEONG KIM ET AL: "Multi-dimensional Visualization and Analysis of Cardiac MR Images During Long-Term Follow-Up", IMAGE ANALYSIS AND RECOGNITION LECTURE NOTES IN COMPUTER SCIENCE;;LNCS, SPRINGER BERLIN HEIDELBERG, BE, vol. 4142, 21 September 2006 (2006-09-21), pages 602 - 611, XP019043819, ISBN: 978-3-540-44894-5 *

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