ArticleStrahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]2019
Automatic image segmentation based on synthetic tissue model for delineating organs at risk in spinal metastasis treatment planning.
Article in Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al], 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images.Yearbook of medical informatics · 2022Pooled it
- Accurate object localization facilitates automatic esophagus segmentation in deep learning.Radiation oncology (London, England) · 2024Article
- Feasibility of Augmented Reality for Pediatric Giant Supratentorial Tumors: A Report of Three Cases.Cureus · 2024Article
- Evaluation of a Dedicated Software "Elements™ Spine SRS, BrainlabFrontiers in oncology · 2022Article
- Training and Validation of Deep Learning-Based Auto-Segmentation Models for Lung Stereotactic Ablative Radiotherapy Using Retrospective Radiotherapy Planning Contours.Frontiers in oncology · 2021Article
- Article
- Clinically Oriented Target Contour Evaluation Using Geometric and Dosimetric Indices Based on Simple Geometric Transformations.Technology in cancer research & treatmentArticle
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6 authors.
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Abstract
purposeOne of the main goals in software solutions for treatment planning is to automatize delineation of organs at risk (OARs). In this pilot feasibility study a clinical validation was made of computed tomography (CT)-based extracranial auto-segmentation (AS) using the Brainlab Anatomical Mapping tool (AM).
methodsThe delineation of nine extracranial OARs (lungs, kidneys, trachea, heart, liver, spinal cord, esophagus) from clinical datasets of 24 treated patients was retrospectively evaluated. Manual delineation of OARs was conducted in clinical routine and compared with AS datasets using AM. The Dice similarity coefficient (DSC) and maximum Hausdorff distance (HD) were used as statistical and geometrical measurements, respectively. Additionally, all AS structures were validated using a subjective qualitative scoring system.
resultsAll patient datasets investigated were successfully processed with the evaluated AS software. For the left lung (0.97 ± 0.03), right lung (0.97 ± 0.05), left kidney (0.91 ± 0.07), and trachea (0.93 ± 0.04), the DSC was high with low variability. The DSC scores of other organs (right kidney, heart, liver, spinal cord), except the esophagus, ranged between 0.7 and 0.9. The calculated HD values yielded comparable results. Qualitative assessment showed a general acceptance in more than 85% of AS OARs-except for the esophagus.
conclusionsThe Brainlab AM software is ready for clinical use in most of the OARs evaluated in the thoracic and abdominal region. The software generates highly conformal structure sets compared to manual contouring. The current study design needs revision for further research.
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