A nnU-Net-based automatic segmentation of FCD type II lesions in 3D FLAIR MRI images

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10260599" target="_blank" >RIV/61989100:27240/25:10260599 - isvavai.cz</a>
Result on the web
<a href="https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1601815/full#sec23" target="_blank" >https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1601815/full#sec23</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3389/frai.2025.1601815" target="_blank" >10.3389/frai.2025.1601815</a>

Result language
angličtina
Original language name
A nnU-Net-based automatic segmentation of FCD type II lesions in 3D FLAIR MRI images
Original language description
Focal cortical dysplasia (FCD) type II is a common cause of epilepsy and is challenging to detect due to its similarities with other brain conditions. Finding these lesions accurately is essential for successful surgery and seizure control. Manual detection is slow and challenging because the MRI features are subtle. Deep learning, especially convolutional neural networks, has shown great potential in automating image classification and segmentation by learning and extracting features. The nnU-Net framework is known for its ability to adapt its settings, including preprocessing, network design, training, and post-processing, to any new medical imaging task. This study employs an automated slice selection approach that ranks axial FLAIR slices by their peak voxel intensity and retains the five highest-ranked slices per scan, thereby focusing the network on lesion-rich slices and uses nnU-Net to automate the segmentation of FCD type II lesions on 3D FLAIR MRI images. The study was conducted on 85 FCD type II subjects and results are evaluated through 5-fold cross-validation. Using nnU-Net's flexible and robust design, this study aims to improve the accuracy and speed of lesion detection, helping with better presurgical evaluations and outcomes for epilepsy patients.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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