Magnetic resonance imaging-based target volume delineation in radiation therapy treatment planning for brain tumors using localized region-based active contour.

PURPOSE To evaluate the clinical application of a robust semiautomatic image segmentation method to determine the brain target volumes in radiation therapy treatment planning. METHODS AND MATERIALS A local robust region-based algorithm was used on MRI brain images to study the clinical target volume (CTV) of several patients. First, 3 oncologists delineated CTVs of 10 patients manually, and the process time for each patient was calculated. The averages of the oncologists' contours were evaluated and considered as reference contours. Then, to determine the CTV through the semiautomatic method, a fourth oncologist who was blind to all manual contours selected 4-8 points around the edema and defined the initial contour. The time to obtain the final contour was calculated again for each patient. Manual and semiautomatic segmentation were compared using 3 different metric criteria: Dice coefficient, Hausdorff distance, and mean absolute distance. A comparison also was performed between volumes obtained from semiautomatic and manual methods. RESULTS Manual delineation processing time of tumors for each patient was dependent on its size and complexity and had a mean (±SD) of 12.33 ± 2.47 minutes, whereas it was 3.254 ± 1.7507 minutes for the semiautomatic method. Means of Dice coefficient, Hausdorff distance, and mean absolute distance between manual contours were 0.84 ± 0.02, 2.05 ± 0.66 cm, and 0.78 ± 0.15 cm, and they were 0.82 ± 0.03, 1.91 ± 0.65 cm, and 0.7 ± 0.22 cm between manual and semiautomatic contours, respectively. Moreover, the mean volume ratio (=semiautomatic/manual) calculated for all samples was 0.87. CONCLUSIONS Given the deformability of this method, the results showed reasonable accuracy and similarity to the results of manual contouring by the oncologists. This study shows that the localized region-based algorithms can have great ability in determining the CTV and can be appropriate alternatives for manual approaches in brain cancer.

[1]  Allen R. Tannenbaum,et al.  Localizing Region-Based Active Contours , 2008, IEEE Transactions on Image Processing.

[2]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[3]  Anthony J. Yezzi,et al.  Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification , 2001, IEEE Trans. Image Process..

[4]  V. Devita,et al.  Cancer : Principles and Practice of Oncology , 1982 .

[5]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Peter L. Choyke,et al.  New Techniques in Oncologic Imaging , 2005 .

[7]  Wolfgang Birkfellner,et al.  Applied Medical Image Processing: A Basic Course , 2010 .

[8]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[9]  N. Ayache,et al.  Atlas-based automatic segmentation of MR images: validation study on the brainstem in radiotherapy context. , 2005, International journal of radiation oncology, biology, physics.

[10]  Bruce J. Gerbi,et al.  Treatment Planning in Radiation Oncology , 2011 .

[11]  Yongmin Kim,et al.  A methodology for evaluation of boundary detection algorithms on medical images , 1997, IEEE Transactions on Medical Imaging.

[12]  Maximilien Vermandel,et al.  Automatic segmentation of pelvic structures from magnetic resonance images for prostate cancer radiotherapy. , 2007, International journal of radiation oncology, biology, physics.

[13]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[14]  Vladimir Pekar,et al.  Automated model-based organ delineation for radiotherapy planning in prostatic region. , 2004, International journal of radiation oncology, biology, physics.

[15]  R. Velthuizen,et al.  Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation. , 2004, International journal of radiation oncology, biology, physics.

[16]  Yueh-Yun Chi,et al.  Comparison of human and automatic segmentations of kidneys from CT images. , 2005, International journal of radiation oncology, biology, physics.

[17]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

[18]  Chunming Li,et al.  Minimization of Region-Scalable Fitting Energy for Image Segmentation , 2008, IEEE Transactions on Image Processing.

[19]  M. Kaus,et al.  82: Evaluation of a Semi-Automated Segmentation Method for Delineation of Organs at Risk and Lymph Node Target Volumes in Head and Neck Radiotherapy Planning , 2006 .

[20]  Jun Chen,et al.  Comparison of automatic and human segmentation of kidneys from CT images , 2002 .

[21]  Dorin Comaniciu,et al.  Robust real-time myocardial border tracking for echocardiography: an information fusion approach , 2004, IEEE Transactions on Medical Imaging.