TITLE

A semiautomatic tool for prostate segmentation in radiotherapy treatment planning

AUTHOR(S)
Schulz, Jörn; Skrøvseth, Stein Olav; Tømmerås, Veronika Kristine; Marienhagen, Kirsten; Godtliebsen, Fred
PUB. DATE
February 2014
SOURCE
BMC Medical Imaging;2014, Vol. 14 Issue 1, p1
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Background Delineation of the target volume is a time-consuming task in radiotherapy treatment planning, yet essential for a successful treatment of cancers such as prostate cancer. To facilitate the delineation procedure, the paper proposes an intuitive approach for 3D modeling of the prostate by slice-wise best fitting ellipses. Methods The proposed estimate is initialized by the definition of a few control points in a new patient. The method is not restricted to particular image modalities but assumes a smooth shape with elliptic cross sections of the object. A training data set of 23 patients was used to calculate a prior shape model. The mean shape model was evaluated based on the manual contour of 10 test patients. The patient records of training and test data are based on axial T1-weighted 3D fast-field echo (FFE) sequences. The manual contours were considered as the reference model. Volume overlap (Vo), accuracy (Ac) (both ratio, range 0-1, optimal value 1) and Hausdorff distance (HD) (mm, optimal value 0) were calculated as evaluation parameters. Results The median and median absolute deviation (MAD) between manual delineation and deformed mean best fitting ellipses (MBFE) was Vo (0.9 ± 0.02), Ac (0.81 ± 0.03) and HD (4.05 ± 1.3)mm and between manual delineation and best fitting ellipses (BFE) was Vo (0.96 ± 0.01), Ac (0.92 ± 0.01) and HD (1.6±0.27)mm. Additional results show a moderate improvement of the MBFE results after Monte Carlo Markov Chain (MCMC) method. Conclusions The results emphasize the potential of the proposed method of modeling the prostate by best fitting ellipses. It shows the robustness and reproducibility of the model. A small sample test on 8 patients suggest possible time saving using the model.
ACCESSION #
94481435

 

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