Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images.
Sci Rep
; 13(1): 13341, 2023 08 16.
Article
em En
| MEDLINE
| ID: mdl-37587160
ABSTRACT
Magnetic resonance imaging (MRI) is widely used for ischemic stroke lesion detection in mice. A challenge is that lesion segmentation often relies on manual tracing by trained experts, which is labor-intensive, time-consuming, and prone to inter- and intra-rater variability. Here, we present a fully automated ischemic stroke lesion segmentation method for mouse T2-weighted MRI data. As an end-to-end deep learning approach, the automated lesion segmentation requires very little preprocessing and works directly on the raw MRI scans. We randomly split a large dataset of 382 MRI scans into a subset (n = 293) to train the automated lesion segmentation and a subset (n = 89) to evaluate its performance. We compared Dice coefficients and accuracy of lesion volume against manual segmentation, as well as its performance on an independent dataset from an open repository with different imaging characteristics. The automated lesion segmentation produced segmentation masks with a smooth, compact, and realistic appearance that are in high agreement with manual segmentation. We report dice scores higher than the agreement between two human raters reported in previous studies, highlighting the ability to remove individual human bias and standardize the process across research studies and centers.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Trabalho de Parto
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Acidente Vascular Cerebral
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Aprendizado Profundo
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AVC Isquêmico
Limite:
Animals
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Female
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Humans
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Pregnancy
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Alemanha