Back to Search
Start Over
TU-C-332-02: Concomitant Segmentation and Registration of Liver Anatomy Using SPECT-CT Imaging
- Source :
- Medical Physics. 35:2894-2894
- Publication Year :
- 2008
- Publisher :
- Wiley, 2008.
-
Abstract
- Purpose: To develop an automatic and accurate technique for concomitant segmentations and registration of liveranatomy using SPECT and CTimages for unsealed sourceradiotherapy.Method and Materials: The link between segmentation and registration is given by the using the level set of a liver segmentation into the registration process. In the combined approach, the liver is automatically segmented from the CTimage by evolving an initial seed with a level set until it locks to the liver's border as observed in the CTimages. The time‐crossing map of the level set is then used to match gradients in the SPECTimage to the level set by using a data structure containing the signed distance values at a small band of neighboring pixels. Results: The technique was applied to three cases of metastatic liver disease treated with unsealed source therapy. Results indicated that the speed map of the level set plays an importance role in obtainng an accurate registration and produce a segmentation that is superior in registration time and accuracy over manual segmentation or the standard registration approach using mutual information. Accuracy measured with the convergence analysis method was of less than 0.5 mm rotation and 1 degree rotation. Conclusion: With the proposed combined segmentation‐registration technique, the uncertainty of soft‐tissue target localization could be greatly reduced ensuring accurate therapy assessment to be precisely delivered as planned. The combined all‐in‐one approach is automated and provides excellent accuracy over manual segmentation and mutual information approaches.
- Subjects :
- Level set (data structures)
Pathology
medicine.medical_specialty
Pixel
medicine.diagnostic_test
Computer science
business.industry
Scale-space segmentation
Signed distance function
Metastatic liver disease
General Medicine
Mutual information
Single-photon emission computed tomography
Level set
Medical imaging
medicine
Segmentation
Computer vision
Artificial intelligence
medicine.symptom
business
Subjects
Details
- ISSN :
- 00942405
- Volume :
- 35
- Database :
- OpenAIRE
- Journal :
- Medical Physics
- Accession number :
- edsair.doi...........1ba825e3eb21ec42256a3b2963eead23
- Full Text :
- https://doi.org/10.1118/1.2962492