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Volume 12
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10.3390/app12178523
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Article

Contour Propagation for Radiotherapy Treatment Planning Using Nonrigid Registration and Parameter Optimization: Case Studies in Liver and Breast Cancer

by
Eliseo Vargas-Bedoya
1,
Juan Carlos Rivera
2,*,
Maria Eugenia Puerta
1,
Aurelio Angulo
3,
Niklas Wahl
4 and
Gonzalo Cabal
3
1
Mathematics and Applications Research Group, Universidad EAFIT, Medellin 050022, Colombia
2
Mathematical Modeling Research Group, Universidad EAFIT, Medellin 050022, Colombia
3
Department of Radiation Therapy, Clínica El Rosario, Medellin 050012, Colombia
4
Research Group Radiotherapy Optimization, German Cancer Research Center, 69120 Heidelberg, Germany
*
Author to whom correspondence should be addressed.
Appl. Sci. 2022, 12(17), 8523; https://doi.org/10.3390/app12178523
Submission received: 25 June 2022 / Revised: 23 July 2022 / Accepted: 25 July 2022 / Published: 26 August 2022
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Abstract

Radiotherapy treatments are carried out using computerized axial tomography. In radiation therapy planning, the radiation oncologist must do a manual segmentation of volumes of interest to delineate the organs that should be irradiated. This way of carrying out the process generates long execution times and introduces a subjective component. In this study, a contour-propagation algorithm is formulated to automate the segmentation, based on elastic registration or nonrigid demon registration. A heuristic algorithm to find the parameters that optimize the registration is also proposed. The parameters found along with the contour-propagation algorithm are able to estimate contours of scans with Dice similarity coefficients (DSC) greater than 0.92 and maintain stability with B-spline registration, which takes in the parameters found as input. The study allows for validating the results using the correlation coefficient (CC) to compare the similarity between the voxels’ gray-scale intensity of the estimated tomography and the original tomography, obtaining values greater than 0.96. These values were validated under medical criteria and applied to liver and breast CT scans, indicating good performance for radiation therapy planning.
Keywords: image registration; nonrigid registration; demons; heuristic methods image registration; nonrigid registration; demons; heuristic methods

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MDPI and ACS Style

Vargas-Bedoya, E.; Rivera, J.C.; Puerta, M.E.; Angulo, A.; Wahl, N.; Cabal, G. Contour Propagation for Radiotherapy Treatment Planning Using Nonrigid Registration and Parameter Optimization: Case Studies in Liver and Breast Cancer. Appl. Sci. 2022, 12, 8523. https://doi.org/10.3390/app12178523

AMA Style

Vargas-Bedoya E, Rivera JC, Puerta ME, Angulo A, Wahl N, Cabal G. Contour Propagation for Radiotherapy Treatment Planning Using Nonrigid Registration and Parameter Optimization: Case Studies in Liver and Breast Cancer. Applied Sciences. 2022; 12(17):8523. https://doi.org/10.3390/app12178523

Chicago/Turabian Style

Vargas-Bedoya, Eliseo, Juan Carlos Rivera, Maria Eugenia Puerta, Aurelio Angulo, Niklas Wahl, and Gonzalo Cabal. 2022. "Contour Propagation for Radiotherapy Treatment Planning Using Nonrigid Registration and Parameter Optimization: Case Studies in Liver and Breast Cancer" Applied Sciences 12, no. 17: 8523. https://doi.org/10.3390/app12178523

APA Style

Vargas-Bedoya, E., Rivera, J. C., Puerta, M. E., Angulo, A., Wahl, N., & Cabal, G. (2022). Contour Propagation for Radiotherapy Treatment Planning Using Nonrigid Registration and Parameter Optimization: Case Studies in Liver and Breast Cancer. Applied Sciences, 12(17), 8523. https://doi.org/10.3390/app12178523

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