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Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models.

Yuka UragoHiroyuki OkamotoTomoya KanedaNaoya MurakamiTairo KashiharaMihiro TakemoriHiroki NakayamaKotaro IijimaTakahito ChibaJunichi KuwaharaShouichi KatsutaSatoshi NakamuraWeishan ChangHidetoshi SaitohHiroshi Igaki
Published in: Radiation oncology (London, England) (2021)
In terms of efficiency, the processing time for head and neck cancers was much shorter than manual delineation. While quantitative evaluation with AI-based segmentation was significantly more accurate than atlas-based for prostate cancer, there was no significant difference for head and neck cancer. According to the results of visual evaluation, less necessity of manual correction in AI-based segmentation indicates that the segmentation efficiency of AI-based model is higher than that of atlas-based model. The effectiveness of the AI-based model can be expected to improve the segmentation efficiency and to significantly shorten the delineation time.
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