DenseLes slice-wise dense network for multiple sclerosis lesion segmentation and classification /
Accurate and reliable segmentation of multiple sclerosis (MS) lesions from magnetic resonance imaging (MRI) is essential for diagnosis and monitoring disease progression. Therefore, a robust and efficient automated approach can rapidly provide information about the patient. Here, a convolutional neu...
Elmentve itt :
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| Dokumentumtípus: | Cikk |
| Megjelent: |
2026
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| Sorozat: | FRONTIERS IN NEUROLOGY
17 |
| Tárgyszavak: | |
| doi: | 10.3389/fneur.2026.1704317 |
| mtmt: | 37029702 |
| Online Access: | http://publicatio.bibl.u-szeged.hu/40052 |
| Tartalmi kivonat: | Accurate and reliable segmentation of multiple sclerosis (MS) lesions from magnetic resonance imaging (MRI) is essential for diagnosis and monitoring disease progression. Therefore, a robust and efficient automated approach can rapidly provide information about the patient. Here, a convolutional neural network-based method is proposed to segment lesions from FLAIR images. The DenseLessystem includes two stages: pre-processing of image data (brain extraction, standardization), then segmentation of MS lesions using an end-to-end slice-wise dense network. We also identified the segmented lesions in specific locations [periventricular, (juxta)cortical, infratentorial, and spinal]. DenseLesis evaluated and compared to other methods on our assembled data and the public MSSEG 2016 MS challenge dataset. Our model demonstrates a significant improvement in segmentation quality over previous approaches, achieving an average Dice score of 0.80% on the Szeged MS dataset. On the MSSEG 2016 dataset, our method achieved Dice scores ranging from 0.32% to 0.73%, comparable to those of human raters. |
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| Terjedelem/Fizikai jellemzők: | 11 |
| ISSN: | 1664-2295 |