Contour Propagation for Radiotherapy Treatment Planning Using Nonrigid Registration and Parameter Optimization: Case Studies in Liver and Breast Cancer
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 t...
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/17/8523 |
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| author | Eliseo Vargas-Bedoya Juan Carlos Rivera Maria Eugenia Puerta Aurelio Angulo Niklas Wahl Gonzalo Cabal |
| author_facet | Eliseo Vargas-Bedoya Juan Carlos Rivera Maria Eugenia Puerta Aurelio Angulo Niklas Wahl Gonzalo Cabal |
| author_sort | Eliseo Vargas-Bedoya |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.92</mn></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.96</mn></mrow></semantics></math></inline-formula>. These values were validated under medical criteria and applied to liver and breast CT scans, indicating good performance for radiation therapy planning. |
| first_indexed | 2024-03-10T03:04:07Z |
| format | Article |
| id | doaj.art-df125bfb6a194dd7875577c4a1ef5e8c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:04:07Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-df125bfb6a194dd7875577c4a1ef5e8c2023-11-23T12:41:05ZengMDPI AGApplied Sciences2076-34172022-08-011217852310.3390/app12178523Contour Propagation for Radiotherapy Treatment Planning Using Nonrigid Registration and Parameter Optimization: Case Studies in Liver and Breast CancerEliseo Vargas-Bedoya0Juan Carlos Rivera1Maria Eugenia Puerta2Aurelio Angulo3Niklas Wahl4Gonzalo Cabal5Mathematics and Applications Research Group, Universidad EAFIT, Medellin 050022, ColombiaMathematical Modeling Research Group, Universidad EAFIT, Medellin 050022, ColombiaMathematics and Applications Research Group, Universidad EAFIT, Medellin 050022, ColombiaDepartment of Radiation Therapy, Clínica El Rosario, Medellin 050012, ColombiaResearch Group Radiotherapy Optimization, German Cancer Research Center, 69120 Heidelberg, GermanyDepartment of Radiation Therapy, Clínica El Rosario, Medellin 050012, ColombiaRadiotherapy 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.92</mn></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.96</mn></mrow></semantics></math></inline-formula>. These values were validated under medical criteria and applied to liver and breast CT scans, indicating good performance for radiation therapy planning.https://www.mdpi.com/2076-3417/12/17/8523image registrationnonrigid registrationdemonsheuristic methods |
| spellingShingle | Eliseo Vargas-Bedoya Juan Carlos Rivera Maria Eugenia Puerta Aurelio Angulo Niklas Wahl Gonzalo Cabal Contour Propagation for Radiotherapy Treatment Planning Using Nonrigid Registration and Parameter Optimization: Case Studies in Liver and Breast Cancer Applied Sciences image registration nonrigid registration demons heuristic methods |
| title | Contour Propagation for Radiotherapy Treatment Planning Using Nonrigid Registration and Parameter Optimization: Case Studies in Liver and Breast Cancer |
| title_full | Contour Propagation for Radiotherapy Treatment Planning Using Nonrigid Registration and Parameter Optimization: Case Studies in Liver and Breast Cancer |
| title_fullStr | Contour Propagation for Radiotherapy Treatment Planning Using Nonrigid Registration and Parameter Optimization: Case Studies in Liver and Breast Cancer |
| title_full_unstemmed | Contour Propagation for Radiotherapy Treatment Planning Using Nonrigid Registration and Parameter Optimization: Case Studies in Liver and Breast Cancer |
| title_short | Contour Propagation for Radiotherapy Treatment Planning Using Nonrigid Registration and Parameter Optimization: Case Studies in Liver and Breast Cancer |
| title_sort | contour propagation for radiotherapy treatment planning using nonrigid registration and parameter optimization case studies in liver and breast cancer |
| topic | image registration nonrigid registration demons heuristic methods |
| url | https://www.mdpi.com/2076-3417/12/17/8523 |
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