Deformable registration of 3D ultrasound volumes using automatic landmark generation.
US image registration is an important task e.g. in Computer Aided Surgery. Due to tissue deformation occurring between pre-operative and interventional images often deformable registration is necessary. We present a registration method focused on surface structures (i.e. saliencies) of soft tissues...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2019-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0213004 |
| _version_ | 1818938456839028736 |
|---|---|
| author | Michael Figl Rainer Hoffmann Marcus Kaar Johann Hummel |
| author_facet | Michael Figl Rainer Hoffmann Marcus Kaar Johann Hummel |
| author_sort | Michael Figl |
| collection | DOAJ |
| description | US image registration is an important task e.g. in Computer Aided Surgery. Due to tissue deformation occurring between pre-operative and interventional images often deformable registration is necessary. We present a registration method focused on surface structures (i.e. saliencies) of soft tissues like organ capsules or vessels. The main concept follows the idea of representative landmarks (so called leading points). These landmarks represent saliencies in each image in a certain region of interest. The determination of deformation was based on a geometric model assuming that saliencies could locally be described by planes. These planes were calculated from the landmarks using two dimensional linear regression. Once corresponding regions in both images were found, a displacement vector field representing the local deformation was computed. Finally, the deformed image was warped to match the pre-operative image. For error calculation we used a phantom representing the urinary bladder and the prostate. The phantom could be deformed to mimic tissue deformation. Error calculation was done using corresponding landmarks in both images. The resulting target registration error of this procedure amounted to 1.63 mm. With respect to patient data a full deformable registration was performed on two 3D-US images of the abdomen. The resulting mean distance error was 2.10 ± 0.66 mm compared to an error of 2.75 ± 0.57 mm from a simple rigid registration. A two-sided paired t-test showed a p-value < 0.001. We conclude that the method improves the results of the rigid registration considerably. Provided an appropriate choice of the filter there are many possible fields of applications. |
| first_indexed | 2024-12-20T06:08:09Z |
| format | Article |
| id | doaj.art-ef9db32bdb434f44a5fa20c69746e0b3 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-20T06:08:09Z |
| publishDate | 2019-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-ef9db32bdb434f44a5fa20c69746e0b32022-12-21T19:50:46ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01143e021300410.1371/journal.pone.0213004Deformable registration of 3D ultrasound volumes using automatic landmark generation.Michael FiglRainer HoffmannMarcus KaarJohann HummelUS image registration is an important task e.g. in Computer Aided Surgery. Due to tissue deformation occurring between pre-operative and interventional images often deformable registration is necessary. We present a registration method focused on surface structures (i.e. saliencies) of soft tissues like organ capsules or vessels. The main concept follows the idea of representative landmarks (so called leading points). These landmarks represent saliencies in each image in a certain region of interest. The determination of deformation was based on a geometric model assuming that saliencies could locally be described by planes. These planes were calculated from the landmarks using two dimensional linear regression. Once corresponding regions in both images were found, a displacement vector field representing the local deformation was computed. Finally, the deformed image was warped to match the pre-operative image. For error calculation we used a phantom representing the urinary bladder and the prostate. The phantom could be deformed to mimic tissue deformation. Error calculation was done using corresponding landmarks in both images. The resulting target registration error of this procedure amounted to 1.63 mm. With respect to patient data a full deformable registration was performed on two 3D-US images of the abdomen. The resulting mean distance error was 2.10 ± 0.66 mm compared to an error of 2.75 ± 0.57 mm from a simple rigid registration. A two-sided paired t-test showed a p-value < 0.001. We conclude that the method improves the results of the rigid registration considerably. Provided an appropriate choice of the filter there are many possible fields of applications.https://doi.org/10.1371/journal.pone.0213004 |
| spellingShingle | Michael Figl Rainer Hoffmann Marcus Kaar Johann Hummel Deformable registration of 3D ultrasound volumes using automatic landmark generation. PLoS ONE |
| title | Deformable registration of 3D ultrasound volumes using automatic landmark generation. |
| title_full | Deformable registration of 3D ultrasound volumes using automatic landmark generation. |
| title_fullStr | Deformable registration of 3D ultrasound volumes using automatic landmark generation. |
| title_full_unstemmed | Deformable registration of 3D ultrasound volumes using automatic landmark generation. |
| title_short | Deformable registration of 3D ultrasound volumes using automatic landmark generation. |
| title_sort | deformable registration of 3d ultrasound volumes using automatic landmark generation |
| url | https://doi.org/10.1371/journal.pone.0213004 |
| work_keys_str_mv | AT michaelfigl deformableregistrationof3dultrasoundvolumesusingautomaticlandmarkgeneration AT rainerhoffmann deformableregistrationof3dultrasoundvolumesusingautomaticlandmarkgeneration AT marcuskaar deformableregistrationof3dultrasoundvolumesusingautomaticlandmarkgeneration AT johannhummel deformableregistrationof3dultrasoundvolumesusingautomaticlandmarkgeneration |