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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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&apos;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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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&apos;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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Frontiers in Artificial Intelligence

ISSN

2624-8212

e-ISSN

2624-8212

Svazek periodika

8

Číslo periodika v rámci svazku

Jun

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

9

Strana od-do

nestránkováno

Kód UT WoS článku

001525877300001

EID výsledku v databázi Scopus

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