Method and system for anatomical object detection using marginal space deep neural networks
US-2015238148-A1 · Aug 27, 2015 · US
US10445904B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-10445904-B2 |
| Application number | US-201715684499-A |
| Country | US |
| Kind code | B2 |
| Filing date | Aug 23, 2017 |
| Priority date | Aug 23, 2016 |
| Publication date | Oct 15, 2019 |
| Grant date | Oct 15, 2019 |
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A method for the automatic generation of synthetic projections of an examination object from at least one three-dimensional data set acquired by way of a medical imaging system. The three-dimensional data set is used as a basis for determining position information relating to the arrangement of structures of the examination object and at least one synthetic projection based on the position information. We also describe a projection-image-ascertaining facility for the automatic generation of synthetic projections and a computer program product for executing the method.
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The invention claimed is: 1. A method of automatically generating synthetic projections of an examination object from at least one three-dimensional data set acquired by a medical imaging system, the method comprising: ascertaining position information relating to an arrangement of structures of the examination object based on the three-dimensional data set; using said position information to determine at least one reference plane through the examination object; and ascertaining at least one synthetic projection of the examination object relative to the at least one reference plane based on the position information. 2. The method according to claim 1 , which comprises determining projection-geometry parameters for at least one synthetic projection based on the position information and generating the synthetic projection based on the three-dimensional data set using the projection-geometry parameters. 3. The method according to claim 1 , wherein the step of ascertaining the position information comprises analyzing the three-dimensional data set with respect to a spatial arrangement of structures of the examination object. 4. The method according to claim 3 , wherein the step of analyzing the three-dimensional data set comprises ascertaining significant anatomic features of the examination object. 5. The method according to claim 4 , wherein the significant anatomic features of the examination object are anatomic landmarks. 6. The method according to claim 1 , which comprises segmenting the three-dimensional data set. 7. The method according to claim 1 , which comprises ascertaining projection-geometry parameters so that the synthetic projection has a defined orientation with respect to the reference plane. 8. The method according to claim 1 , which comprises generating a plurality of synthetic projections and ascertaining therefrom position information on at least one synthetic projection optimized with respect to specific optimization criteria. 9. The method according to claim 8 , which comprises ascertaining in each case position information from the synthetic projections with reference to a determination of an entropy of pixel values. 10. The method according to claim 8 , which comprises ascertaining in each case position information from the synthetic projections with reference to edges of the structures. 11. The method according to claim 8 , which comprises ascertaining in each case position information from the synthetic projections with reference to a projection surface of the structures. 12. The method according to claim 1 , which comprises using different three-dimensional data sets of the examination object acquired in different positions of the examination object. 13. The method according to claim 1 , which comprises ascertaining preferred projection-geometry parameters with the aid of a machine learning method. 14. The method according to claim 13 , which comprises ascertaining a preferred orientation with respect to a reference plane. 15. The method according to claim 13 , wherein the machine learning method is based on a database. 16. A computer program product, comprising: a non-transitory computer-readable medium storing computer-readable program code configured to cause a computing unit, forming a data-processing arrangement, to carry out the method according to claim 1 when the program code is executed on the computing unit. 17. The computer program product according to claim 16 , wherein the computing unit is a computing unit of a projection-image-ascertaining facility for the automatic generation of synthetic projections of an examination object. 18. The method of claim 1 , wherein a fit method is used to adapt the at least one reference plane to the position information. 19. A projection-image-ascertaining facility for the automatic generation of synthetic projections of an examination object from at least one three-dimensional data set acquired by way of a medical imaging system, the projection-image-ascertaining facility comprising: an input interface for receiving the three-dimensional data set; a computing unit configured for ascertaining position information relating to an arrangement of structures of the examination object based on the three-dimensional data set, for determining at least one reference plane through the examination object using said position information, and for ascertaining at least one synthetic projection of the examination object with respect to the at least one reference plane based on the position information; and an output interface for outputting the synthetic projection. 20. A computer-readable medium, comprising program segments stored in non-transitory form and configured to be read and executed by a computing unit, said program segments being configured to carry out all steps of the method according to claim 1 when the program segments are executed by the computing unit.
Image post-processing, e.g. metal artefact correction · CPC title
Image preprocessing, e.g. calibration, positioning of sources or scatter correction · CPC title
involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging · CPC title
Devices using data or image processing specially adapted for radiation diagnosis · CPC title
by tomography, i.e. reconstruction of 3D images from 2D projections (A61B5/0066 takes precedence) · CPC title
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