A. Apte, R. Al-Lozi, G. Pereira, Matthew E. Johnson, D. Mansur, I. E. Naqa
{"title":"A Graphical Tool and Methods for Assessing Margin Definition From Daily Image Deformations","authors":"A. Apte, R. Al-Lozi, G. Pereira, Matthew E. Johnson, D. Mansur, I. E. Naqa","doi":"10.3933/JROI-2-1-7","DOIUrl":"https://doi.org/10.3933/JROI-2-1-7","url":null,"abstract":"Estimating the proper margins for the planning target volume (PTV) could be a challenging task in cases where the organ undergoes significant changes during the course of radiotherapy treatment. Developments in image-guidance and the presence of onboard imaging technologies facilitate the process of correcting setup errors. However, estimation of errors to organ motions remain an open question due to the lack of proper software tools to accompany these imaging technological advances. Therefore, we have developed a new tool for visualization and quantification of deformations from daily images. The tool allows for estimation of tumor coverage and normal tissue exposure as a function of selected margin (isotropic or anisotropic). Moreover, the software allows estimation of the optimal margin based on the probability of an organ being present at a particular location. Methods based on swarm intelligence, specifically Ant Colony Optimization (ACO) are used to provide an efficient estimate of the optimal margin extent in each direction. ACO can provide global optimal solutions in highly nonlinear problems such as margin estimation. The proposed method is demonstrated using cases from gastric lymphoma with daily TomoTherapy megavoltage CT (MVCT) contours. Preliminary results using Dice similarity index are promising and it is expected that the proposed tool will be very helpful and have significant impact for guiding future margin definition protocols.\u0000","PeriodicalId":426862,"journal":{"name":"Journal of Radiation Oncology Informatics","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123067034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deshan Yang, Jie Zheng, Ahmad Nofal, J. Deasy, I. E. Naqa
{"title":"Techniques and software tool for 3D multimodality medical image segmentation","authors":"Deshan Yang, Jie Zheng, Ahmad Nofal, J. Deasy, I. E. Naqa","doi":"10.3933/JROI-1-1-4","DOIUrl":"https://doi.org/10.3933/JROI-1-1-4","url":null,"abstract":"The era of noninvasive diagnostic radiology and image-guided radiotherapy has witnessed burgeoning interest in applying different imaging modalities to stage and localize complex diseases such as atherosclerosis or cancer. It has been observed that using complementary information from multimodality images often significantly improves the robustness and accuracy of target volume definitions in radiotherapy treatment of cancer. In this work, we present techniques and an interactive software tool to support this new framework for 3D multimodality medical image segmentation. To demonstrate this methodology, we have designed and developed a dedicated open source software tool for multimodality image analysis MIASYS. The software tool aims to provide a needed solution for 3D image segmentation by integrating automatic algorithms, manual contouring methods, image preprocessing filters, post-processing procedures, user interactive features and evaluation metrics. The presented methods and the accompanying software tool have been successfully evaluated for different radiation therapy and diagnostic radiology applications.","PeriodicalId":426862,"journal":{"name":"Journal of Radiation Oncology Informatics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128624441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Automated contrast painting for position verification in radiotherapy","authors":"P. Putora, L. Plasswilm, L. Paulis","doi":"10.3933/JROI-1-1-3","DOIUrl":"https://doi.org/10.3933/JROI-1-1-3","url":null,"abstract":"The influence of information technology in medicine has been constantly rising and represents a central part in many medical disciplines, especially radio-oncology. For the proper delivery of radiation treatment, the correct position of the patient is essential. To verify the correct position of the patient radiological images are made. In order to compare positions, contours of structures (often bones) may be used, these need to be identified and painted. The software that was provided with the linear accelerator contains a bitmap paint program, where these structures are painted manually. This manual painting of structures could be replaced by automated algorithms. However, amendments, innovations or customization of the original software are costly and difficult to achieve due to copyright, license and certification issues. The concept described here aims to get around these issues by creating an automated algorithm on the user level, with no interference of the underlying original software. This system uses the Java platform; with the help of the Java Robot class user input can be simulated. The developed tool proved to be time-saving, functional and the development could easily be accomplished and individually tailored to users needs.","PeriodicalId":426862,"journal":{"name":"Journal of Radiation Oncology Informatics","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116570479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Radiomics in oncology - uncovering tumor phenotype from medical images: a short introduction","authors":"M. Pavic, J. V. van Timmeren","doi":"10.5166/jroi.11.1.2","DOIUrl":"https://doi.org/10.5166/jroi.11.1.2","url":null,"abstract":"Radiomics is a promising method to quantify and describe the tumor phenotype on medical images. High numbers of image features are extracted from medical images and can be used within a clinical decision support system by integrating this data with clinical and pathological variables. Herein, we give a short introduction into this image analysis method and present an overview on the workflow.","PeriodicalId":426862,"journal":{"name":"Journal of Radiation Oncology Informatics","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121481261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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