Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images

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...

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Bibliographic Details
Published in:Scientific reports 2023-08, Vol.13 (1), p.13341-13341, Article 13341
Main Authors: An, Jeehye, Wendt, Leo, Wiese, Georg, Herold, Tom, Rzepka, Norman, Mueller, Susanne, Koch, Stefan Paul, Hoffmann, Christian J., Harms, Christoph, Boehm-Sturm, Philipp
Format: Article
Language:English
Subjects:
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Automation
Deep learning
Humanities and Social Sciences
Image processing
Ischemia
Lesions
Magnetic resonance imaging
multidisciplinary
Science
Science (multidisciplinary)
Segmentation
Stroke
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-39826-8