Enhancing stroke lesion detection and segmentation through nnU-net and multi-modal MRI Analysis2023-2024
Lounès Meddahi, Stéphanie s Leplaideur, Arthur Masson, Isabelle Bonan, Elise Bannier, and Francesca Galassi.
[Oral presentation] 13th World Congress for Neurorehabilitation, World federation for Neurorehabilitation, 2024.
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Stroke lesion segmentation is crucial for diagnosing and treating stroke patients. Manual segmentation is labor-intensive and time-consuming for radiologists. Current deep learning methods for automation have limitations, as they don't fully exploit 3D spatial coherence or MRI modalities' complementary nature. We present a novel method, integrating T1-weighted and FLAIR MRI modalities through a custom algorithm for stroke lesion segmentation. Our approach pretrains the model on a large dataset, then fine-tunes it using in-house data with T1 and FLAIR modalities.