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

Radiation Oncology

Kotaro Iijima¹  Satoshi Nakamura¹  Hiroyuki Okamoto¹  Takahito Chiba¹  Junichi Kuwahara¹  Tairo Kashihara²  Hiroshi Igaki²  Tomoya Kaneda²  Naoya Murakami²  Weishan Chang³  Hidetoshi Saitoh³  Mihiro Takemori³  Hiroki Nakayama³  Yuka Urago³  Shouichi Katsuta⁴ 

[1] Department of Medical Physics, National Cancer Center Hospital;Department of Radiation Oncology, National Cancer Center Hospital;Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University;Department of Radiological Technology, National Cancer Center Hospital;

关键词: Artificial intelligence;    Automatic segmentation;    Deep learning neural network;    Prostate cancer;    Head and neck cancer;   

DOI  :  10.1186/s13014-021-01896-1

来源: DOAJ

【 摘 要 】

Abstract Background Contour delineation, a crucial process in radiation oncology, is time-consuming and inaccurate due to inter-observer variation has been a critical issue in this process. An atlas-based automatic segmentation was developed to improve the delineation efficiency and reduce inter-observer variation. Additionally, automated segmentation using artificial intelligence (AI) has recently become available. In this study, auto-segmentations by atlas- and AI-based models for Organs at Risk (OAR) in patients with prostate and head and neck cancer were performed and delineation accuracies were evaluated. Methods Twenty-one patients with prostate cancer and 30 patients with head and neck cancer were evaluated. MIM Maestro was used to apply the atlas-based segmentation. MIM Contour ProtégéAI was used to apply the AI-based segmentation. Three similarity indices, the Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean distance to agreement (MDA), were evaluated and compared with manual delineations. In addition, radiation oncologists visually evaluated the delineation accuracies. Results Among patients with prostate cancer, the AI-based model demonstrated higher accuracy than the atlas-based on DSC, HD, and MDA for the bladder and rectum. Upon visual evaluation, some errors were observed in the atlas-based delineations when the boundary between the small bowel or the seminal vesicle and the bladder was unclear. For patients with head and neck cancer, no significant differences were observed between the two models for almost all OARs, except small delineations such as the optic chiasm and optic nerve. The DSC tended to be lower when the HD and the MDA were smaller in small volume delineations. Conclusions 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.

【 授权许可】

Unknown