Image Segmentation in Radiation Oncology: Challenges and Progress
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".
Deadline for manuscript submissions: closed (15 January 2022) | Viewed by 345
Special Issue Editor
Interests: image segmentation; image synthesis; image reconstruction; deep learning; adaptive radiation therapy; MRI-based radiation therapy; proton radiation therapy; stereotactic radiosurgery; brachytherapy
Special Issue Information
Dear Colleagues,
It is our pleasure to announce the opening of a new Special Issue in the Applied Sciences journal.
The main topic of this issue will be the current progress and challenges of medical image segmentation in facilitating workflow in radiation oncology. Medical imaging has been progressively integrated into every stage of radiation oncology. Novel imaging modalities are being introduced in this field to meet unique clinical needs. The increasingly high involvement of medical images potentially enables advanced clinical applications, while most of them require timely and accurate localization and delineation of the regions of interest, such as lesions and organs. Manual segmentation is time-consuming, labor-intensive, and prone to inter- and intra-observer variation. Auto-segmentation has been investigated for decades with the aim of achieving fast, precise, and consistent performance. Recent years have witnessed the trend of deep learning being increasingly used in the application of medical imaging segmentation. The latest networks and techniques have been borrowed from the field of computer vision and adapted to specific segmentation tasks in radiation oncology. Although promising results have been shown in various applications, there are some open questions to be answered in future studies.
Dr. Tonghe Wang
Guest Editor
Manuscript Submission Information
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Keywords
- atlas-based image segmentation
- model-based image segmentation
- learning-based image segmentation
- multimodality image segmentation
- image segmentation in treatment planning
- image segmentation in adaptive radiation therapy
- image segmentation in treatment response evaluation
- image segmentation in brachytherapy
- image segmentation in radiomics
