Comparative Assessment of Manual Segmentation of Cerebral Infarction Lesions in Experimental Animals Based on Magnetic Resonance Imaging Using Artificial Intelligence.

I L Gubskiy, D D Namestnikova, E A Cherkashova, I S Gumin, V V Kurilo, V P Chekhonin, K N Yarygin

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Article in Bulletin of experimental biology and medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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7 authors.

I L GubskiyFederal Center of Brain Research and Neurotechnologies, Federal Medical-Biological Agency of Russia, Moscow, Russia. gubskiy.ilya@gmail.com.

D D NamestnikovaFederal Center of Brain Research and Neurotechnologies, Federal Medical-Biological Agency of Russia, Moscow, Russia.

E A CherkashovaFederal Center of Brain Research and Neurotechnologies, Federal Medical-Biological Agency of Russia, Moscow, Russia.

I S GuminFederal Center of Brain Research and Neurotechnologies, Federal Medical-Biological Agency of Russia, Moscow, Russia.

V V KuriloPirogov Russian National Research Medical University, Ministry of Health of the Russian Federation, Moscow, Russia.

V P ChekhoninPirogov Russian National Research Medical University, Ministry of Health of the Russian Federation, Moscow, Russia.

K N YaryginInstitute of Biomedical Chemistry, Moscow, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Artificial IntelligenceCerebral InfarctionImage Processing, Computer-AssistedMagnetic Resonance ImagingAnimalsBrainDisease Models, AnimalMaleRatsReproducibility of Resultsartificial intelligenceexperimental cerebral infarction modelmagnetic resonance imagingmanual segmentationmorphometry

Identifiers

PMID40148667

DOI10.1016/j.mri.2012.05.001

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