Abstract

The goal of radiation therapy is to eradicate tumors while sparing normal tissues from radiation damage. Therefore, accurate definition of tumor and organ boundaries is important for achieving good treatment outcome. Segmentation of tumors and organs at risk is a standard procedure done by radiation oncologists for radiation therapy treatment planning, for which computed tomography (CT) images are commonly used. In this paper, we present the development of CTScanTool, the organ segmentation tool with a graphical user interface, allowing semi-automated and manual delineation. The proposed semi-automated segmentation method is based on the combined techniques of region growing, morphological reconstruction, and watershed transform algorithms. The semi-automated segmentation results of CT images were compared with those contoured by experienced radiation oncologists. In the experiment, we investigated the combined segmentation method on brain, lung and kidney tissues. In all cases, the true positive rate (sensitivity) was higher than 95%. Jaccard indices and positive predictive values (precision) were higher than 91% for brain and lung tissues and better than 81% for kidney tissues. In the near future, CTScanTool will be incorporated in the Proton pencil beam Scanning treatment PLANning system (PSPLAN) used for research and education in proton therapy.

Details

Title
CTScanTool, a semi-automated organ segmentation tool for radiotherapy treatment planning
Author
Liamsuwan, T 1 ; Tantisatirapong, S 2 ; Tangboonduangjit, P 3 

 Nuclear Research and Development Division, Thailand Institute of Nuclear Technology, Nakhon Nayok 26120 Thailand; Faculty of Medicine and Public Health, HRH Princess Chulabhorn College of Medical Science (PCCMS), Chulabhorn Royal Academy, Bangkok 10210 Thailand 
 Department of Biomedical Engineering, Faculty of Engineering, Srinakharinwirot University, Nakhon Nayok 26120 Thailand 
 Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok 10400 Thailand 
Publication year
2019
Publication date
Aug 2019
Publisher
IOP Publishing
ISSN
17426588
e-ISSN
17426596
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2567799676
Copyright
© 2019. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.