|
Feature Point Selection and Motion Models for 2-D/3-D Registration In interventional radiology, live X-ray images are used
in order to guide the surgeon during the procedure.
However, important anatomical structures may not be
visible in these images. To visualize these structures,
pre-operatively acquired 3-D images, such as CT- or MRI-
scans, can be overlaid with the 2-D image. 2-D/3-D
registration methods are used in order to estimate the
pose of the 3-D image for the overlay. A feature-based
registration method has been developed at the LME which
can cope especially well with registration using a single
2-D image. This method makes use of a motion model which
is able to estimate rigid 3-D transformations from 2-D
displacements of a set of points.
In this research project, feature point selection methods
and motion model extensions are explored which can
further improve and extend the robustness as well as the
accuracy of the registration method. Although different
feature extraction methods are described in the
literature, the setting in medical 2-D/3-D registration
is unique in that the imaged objects are
translucent for the X-ray imaging system. The selection
of good points is done dependent on the use case. This
includes feature selection depending on the anatomical
structures which are registered as well as multi-modal
registration, where the feature-matching is more
challenging due to different intensity distributions for
the same anatomical structures. Extensions of the motion
model are also considered to make optimal use of the
displacement information which can be gained depending on
the feature properties (for example, only a 1-D component
of the displacement can be estimated at edge points due
to the aperture problem while 2-D displacement can be
estimated at corners). | Project manager: Prof. Dr.-Ing. habil. Andreas Maier
Project participants: Roman Schaffert, M. Sc., Jian Wang, M. Sc., Dr.-Ing. Anja Borsdorf
Keywords: Rigid; Registration; Fluoroscopy; CT; Feature-Based; 2-D/3-D; Motion Estimation;
Duration: 1.8.2015 - 31.7.2019
Sponsored by: Siemens Healthineers AG
Contact: Schaffert, Roman Phone +49 9131 85 27826, Fax +49 9131 85 27270, E-Mail: roman.schrom.schaffert@fau.de
| Publications |
---|
Schaffert, Roman ; Wang, Jian ; Fischer, Peter ; Borsdorf, Anja ; Maier, Andreas: Multi-View Depth-Aware Rigid 2-D/3-D Registration. In: IEEE (Ed.) : 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC) (IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) Atlanta, Georgia, USA 26.10.2017). 2017, pp -. | Schaffert, Roman ; Wang, Jian ; Borsdorf, Anja ; Hornegger, Joachim ; Maier, Andreas: Comparison of Rigid Gradient-Based 2D/3D Registration Using Projection and Back-Projection Strategies. In: Tolxdorff, Thomas ; Deserno, M. Thomas ; Handels, Heinz ; Meinzer, Hans-Peter (Ed.) : Bildverarbeitung für die Medizin (Workshop Bildverarbeitung für die Medizin Berlin 13.03.2016). Springer : Springer, 2016, pp 140-145. [doi>10.1007/978-3-662-49465-3_26] | Schaffert, Roman ; Wang, Jian ; Fischer, Peter ; Borsdorf, Anja ; Maier, Andreas: Metric-Driven Learning of Correspondence Weighting for 2-D/3-D Image Registration. In: Springer (Ed.) : Pattern Recognition, 40th German Conference (40th German Conference on Pattern Recognition (GCPR 2018) Stuttgart 10.10.2018-12.10.2018). 2018, pp 1-13. |
Institution: Chair of Computer Science 5 (Pattern Recognition)
|
|
|