ArticleJournal of medical imaging (Bellingham, Wash.)2016
Iterative-cuts: longitudinal and scale-invariant segmentation via user-defined templates for rectosigmoid colon in gynecological brachytherapy.
Article in Journal of medical imaging (Bellingham, Wash.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- The clinical evaluation of atlas-based auto-segmentation for automatic contouring during cervical cancer radiotherapy.Frontiers in oncology · 2022Article
- Interactive Outlining of Pancreatic Cancer Liver Metastases in Ultrasound Images.Scientific reports · 2017Article
- Iterative-cuts: longitudinal and scale-invariant segmentation via user-defined templates for rectosigmoid colon in gynecological brachytherapy.Journal of medical imaging (Bellingham, Wash.) · 2016Article
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Abstract
Among all types of cancer, gynecological malignancies belong to the fourth most frequent type of cancer among women. In addition to chemotherapy and external beam radiation, brachytherapy is the standard procedure for the treatment of these malignancies. In the progress of treatment planning, localization of the tumor as the target volume and adjacent organs of risks by segmentation is crucial to accomplish an optimal radiation distribution to the tumor while simultaneously preserving healthy tissue. Segmentation is performed manually and represents a time-consuming task in clinical daily routine. This study focuses on the segmentation of the rectum/sigmoid colon as an organ-at-risk in gynecological brachytherapy. The proposed segmentation method uses an interactive, graph-based segmentation scheme with a user-defined template. The scheme creates a directed two-dimensional graph, followed by the minimal cost closed set computation on the graph, resulting in an outlining of the rectum. The graph's outline is dynamically adapted to the last calculated cut. Evaluation was performed by comparing manual segmentations of the rectum/sigmoid colon to results achieved with the proposed method. The comparison of the algorithmic to manual result yielded a dice similarity coefficient value of [Formula: see text], in comparison to [Formula: see text] for the comparison of two manual segmentations by the same physician. Utilizing the proposed methodology resulted in a median time of [Formula: see text], compared to 300 s needed for pure manual segmentation.
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