Gubskiy I L, Namestnikova D D, Cherkashova E A, Gumin I S, Kurilo V V, Chekhonin V P, Yarygin K N
Federal Center of Brain Research and Neurotechnologies, Federal Medical-Biological Agency of Russia, Moscow, Russia.
Pirogov Russian National Research Medical University, Ministry of Health of the Russian Federation, Moscow, Russia.
Bull Exp Biol Med. 2025 Feb;178(4):514-519. doi: 10.1007/s10517-025-06366-2. Epub 2025 Mar 28.
The aim of this study was to evaluate the quality of manual segmentation of cerebral infarction lesions in experimental animals with modeled brain infarct based on magnetic resonance imaging compared to an automated artificial intelligence approach. For automated infarct segmentation, an artificial intelligence system with the Swin-UNETR architecture was used, while manual segmentation was performed by four independent researchers. It was shown that manual segmentation exhibits significant variability, especially when small brain infarct lesions are analyzed. The obtained data emphasize the need for standardizing methods and applying automated systems to improve the accuracy and reproducibility of the results.
本研究的目的是基于磁共振成像，评估与自动人工智能方法相比，在模拟脑梗死的实验动物中脑梗死病变手动分割的质量。对于自动梗死分割，使用了具有Swin-UNETR架构的人工智能系统，而手动分割由四名独立研究人员进行。结果表明，手动分割存在显著差异，尤其是在分析小脑梗死病变时。所获得的数据强调了标准化方法和应用自动系统以提高结果的准确性和可重复性的必要性。
