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Verfasst von:Fränzle, Andrea [VerfasserIn]   i
 Bretschi, Maren [VerfasserIn]   i
 Bäuerle, Tobias [VerfasserIn]   i
 Giske, Kristina [VerfasserIn]   i
 Hillengaß, Jens [VerfasserIn]   i
 Bendl, Rolf [VerfasserIn]   i
Titel:A quantification strategy for missing bone mass in case of osteolytic bone lesions
Verf.angabe:Andrea Fränzle, Maren Bretschi and Tobias Bäuerle, Kristina Giske, Jens Hillengass, Rolf Bendl
E-Jahr:2013
Jahr:11 November 2013
Umfang:8 S.
Teil:volume:40
 year:2013
 number:12
 elocationid:123501
 pages:123501-1-123501-8
 extent:8
Fussnoten:Gesehen am 26.04.2021
Titel Quelle:Enthalten in: Medical physics
Ort Quelle:Hoboken, NJ : Wiley, 1974
Jahr Quelle:2013
Band/Heft Quelle:40(2013), 12, Artikel-ID 123501, Seite 123501-1-123501-8
ISSN Quelle:2473-4209
 1522-8541
Abstract:Purpose: Most of the patients who died of breast cancer have developed bone metastases. To understand the pathogenesis of bone metastases and to analyze treatment response of different bone remodeling therapies, preclinical animal models are examined. In breast cancer, bone metastases are often bone destructive. To assess treatment response of bone remodeling therapies, the volumes of these lesions have to be determined during the therapy process. The manual delineation of missing structures, especially if large parts are missing, is very time-consuming and not reproducible. Reproducibility is highly important to have comparable results during the therapy process. Therefore, a computerized approach is needed. Also for the preclinical research, a reproducible measurement of the lesions is essential. Here, the authors present an automated segmentation method for the measurement of missing bone mass in a preclinical rat model with bone metastases in the hind leg bones based on 3D CT scans. Methods: The affected bone structure is compared to a healthy model. Since in this preclinical rat trial the metastasis only occurs on the right hind legs, which is assured by using vessel clips, the authors use the left body side as a healthy model. The left femur is segmented with a statistical shape model which is initialised using the automatically segmented medullary cavity. The left tibia and fibula are segmented using volume growing starting at the tibia medullary cavity and stopping at the femur boundary. Masked images of both segmentations are mirrored along the median plane and transferred manually to the position of the affected bone by rigid registration. Affected bone and healthy model are compared based on their gray values. If the gray value of a voxel indicates bone mass in the healthy model and no bone in the affected bone, this voxel is considered to be osteolytic. Results: The lesion segmentations complete the missing bone structures in a reasonable way. The mean ratiovr/vm of the reconstructed bone volume vr and the healthy model bone volume vm is 1.07, which indicates a good reconstruction of the modified bone. Conclusions: The qualitative and quantitative comparison of manual and semi-automated segmentation results have shown that comparing a modified bone structure with a healthy model can be used to identify and measure missing bone mass in a reproducible way.
DOI:doi:10.1118/1.4828843
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext ; Verlag ; Resolving-System: https://doi.org/10.1118/1.4828843
 Volltext ; Verlag: https://doi.org/https://doi.org/10.1118/1.4828843
 Volltext: https://aapm.onlinelibrary.wiley.com/doi/abs/10.1118/1.4828843
 DOI: https://doi.org/10.1118/1.4828843
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Acceleration measurement
 Analysing materials by determining density or specific gravity
 Biological material
 Biomedical modeling
 bone
 bone metastasis
 Cancer
 Computed tomography
 Computer modeling
 Computer software
 Computerised tomographs
 computerised tomography
 Decision trees
 Digital computing or data processing equipment or methods
 e.g. blood
 Haemocytometers
 image analysis
 Image data processing or generation
 image reconstruction
 image registration
 image segmentation
 in general
 Investigating density or specific gravity of materials
 mass measurement
 Mass measurement
 medical image processing
 Medical image segmentation
 Medical imaging
 osteolytic lesions
 physiological models
 preclinical animal model
 Reconstruction
 Registration
 segmentation
 Segmentation
 specially adapted for specific applications
 statistical analysis
 urine
K10plus-PPN:1755950128
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