Method, Apparatus and Computer Program Product for Automatic Segmenting of Cardiac Chambers

• US 20070253609A1
• Filed: 04/28/2006
• Published: 11/01/2007
• Est. Priority Date: 04/28/2006
• Status: Active Grant

• Current Assignee: PIE Medical Imaging BV
• Original Assignee: PIE Medical Imaging BV
• Inventors: Aben, Jean-Paul
• Granted Patent: US 7,864,997 B2
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: G06T 2207/10076 4D tomography; Time-sequential 3D tomography
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/30048 Heart; Cardiac
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/149 involving deformable models, e.g. active contour models

Abstract

A method, apparatus and computer program product for machine-based segmentation of the cardiac chambers in a four-dimensional magnetic resonance image data that contains at least short-axis cine sequences. The segmenting is based on temporal variations in the data set and on features extracted from the image data. In particular, the method and apparatus process the image data to automatically segment an endocard based on fuzzy object extraction and to automatically segment an epicard through finding a radial minimum cost with dynamic sign definition and tissue classification. In addition, image data is preferably preprocessed to eliminate high local image variances and inhomogeneity. The image data is also preferably processed to identify positions of a base slice, an apex slice, an end-diastole, an end-systole and an LVRV connection.

Claims

1. A method for machine-based segmentation of cardiac chambers in a four-dimensional magnetic resonance image that contains at least short-axis cine sequences, the segmentation based on temporal variations in the image, the method comprising:

preprocessing image data to eliminate high local image variances and in homogeneity;

processing image data to identify positions of a base slice, an apex slice, an end-diastole phase, an end-systole phase and an LVRV connection, the LVRV connection being a posterior junction between a right ventricle and the interventricular septum;

followed by machine-based segmentation of an endocard based on fuzzy object extraction, and machine-based segmentation of an epicard through finding a radial minimum cost with dynamic sign definition and tissue classification.

2. A method as claimed in claim 1, wherein:

said cine sequences furthermore comprise two-chamber cine sequences and four-chamber cine sequences.

3. A method as claimed in claim 1, further comprising:

correcting for data acquisition movements.

4. A method as claimed in claim 1, further comprising:

automatic segmentation of the papillary muscles of the heart.

5. A method as claimed in claim 4, further comprising:

automatic detection of the position of the LVRV connection.

6. A method as claimed in claim 1, further comprising:

allowing for operator inspection of a subject-caused movement in the acquired MR image dataset by using system coordinates from an MR scanner apparatus.

7. A method as claimed in claim 6, further comprising:

automatic correction for subject-caused movement in the acquired MR image dataset by using system coordinates from an MR scanner apparatus.

8. A method as claimed in claim 1, wherein:

segmentation of the endocard and epicard starts from an operator-defined seed contour and being based on radial minimum cost with dynamic sign definition and tissue classification.

9. A method as claimed in claim 1, wherein:

volume of a ventricle is determined by a correction that is derived from the spatial location of the heart'"'"'s apex position on a curve which is obtained from the long axis cine sequences.

10. A method as claimed in claim 9, wherein:

said correction takes into account a mitral valve position obtained from long axis cine views.

11. An apparatus for machine-based segmentation of cardiac chambers in a four-dimensional magnetic resonance image that contains at least short-axis cine sequences, the segmentation based on temporal variations in the data set and on features extracted from the image, the apparatus comprising:

preprocessing means for preprocessing image data to eliminate high local image variances and inhomogeneity;

means for processing image data to identify positions of a base slice, an apex slice, an end-diastole, an end-systole and an LVRV connection, the LVRV connection being a posterior junction between a right ventricle and an interventricular septum, and

means for machine-based segmentation of an endocard based on fuzzy object extraction, and means for machine-based segmentation of an epicard through finding a radial minimum cost with dynamic sign definition and tissue classification.

12. An apparatus as claimed in claim 11, wherein:

said cine sequences furthermore comprise two-chamber cine sequences and four-chamber cine sequences.

13. An apparatus as claimed in claim 11, wherein:

machine-based segmentation over time of endocardial, epicardial and papillary muscles is based on a seed contour.

14. A computer program product that is installable onto a computer processing platform, the computer program product readable by the computer processing platform, tangibly embodying a program of instructions executable by the computer processing platform to perform methods steps for machine-based segmentation of the cardiac chambers in four-dimensional magnetic resonance image that contains at least short-axis cine sequences, the segmentation based on temporal variations in the image and on features extracted from the image, the method steps comprising:

preprocessing image data to eliminate high local image variances and in homogeneity;

processing image data to identify positions of a base slice, an apex slice, an end-diastole phase, an end-systole phase and an LVRV connection, the LVRV connection being a posterior junction between a right ventricle and the interventricular septum;

followed by machine-based segmentation of an endocard based on fuzzy object extraction, and machine-based segmentation of an epicard through finding a radial minimum cost with dynamic sign definition and tissue classification.

15. A computer program product as claimed in claim 14, the method steps further comprising:

correcting for data acquisition movements.

16. A computer program product as claimed in claim 14, the method steps further comprising:

automatic segmentation of the papillary muscles of the heart.

17. A computer program product as claimed in claim 14, the method steps further comprising:

allowing for operator inspection of a subject-caused movement in the acquired MR image dataset by using system coordinates from an MR scanner apparatus.

18. A computer program product as claimed in claim 17, the method steps further comprising:

automatic correction for subject-caused movement in the acquired MR image dataset by using system coordinates from an MR scanner apparatus.

19. A method for machine-based segmentation of cardiac chambers in a four-dimensional magnetic resonance image that contains short-axis cine sequences, two-chamber cine sequences and four-chamber cine sequences, the segmentation based on temporal variations in the image and on features extracted from the image, the method comprising:

interacting with an operator to indicate the epicard and endocard in a long axis data set; and

automatic segmentation of the endocard based on fuzzy object extraction, and automatic segmentation of the epicard based on radial minimum cost with dynamic sign definition and tissue classification.

20. An apparatus for machine-based segmentation of cardiac chambers in a four-dimensional magnetic resonance image that contains short-axis cine sequences, two-chamber cine sequences and four-chamber cine sequences, the segmentation based on temporal variations in the image and on features extracted from the image, the apparatus comprising:

means for interacting with an operator to indicate the epicard and endocard in a long axis data set; and

means for automatic segmentation of the endocard based on fuzzy object extraction, and means for automatic segmentation of the epicard based on radial minimum cost with dynamic sign definition and tissue classification.