Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI
Abstract Automated segmentation of pediatric brain tumors (PBTs) can support precise diagnosis and treatment monitoring, but it is still poorly investigated in literature. This study proposes two different Deep Learning approaches for semantic segmentation of tumor regions in PBTs from MRI scans. Tw...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-07257-2 |
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| author | Annachiara Cariola Elena Sibilano Andrea Guerriero Vitoantonio Bevilacqua Antonio Brunetti |
| author_facet | Annachiara Cariola Elena Sibilano Andrea Guerriero Vitoantonio Bevilacqua Antonio Brunetti |
| author_sort | Annachiara Cariola |
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| description | Abstract Automated segmentation of pediatric brain tumors (PBTs) can support precise diagnosis and treatment monitoring, but it is still poorly investigated in literature. This study proposes two different Deep Learning approaches for semantic segmentation of tumor regions in PBTs from MRI scans. Two pipelines were developed for segmenting enhanced tumor (ET), tumor core (TC), and whole tumor (WT) in pediatric gliomas from the BraTS-PEDs 2024 dataset. First, a pre-trained SegResNet model was retrained with a transfer learning approach and tested on the pediatric cohort. Then, two novel multi-encoder architectures leveraging the attention mechanism were designed and trained from scratch. To enhance the performance on ET regions, an ensemble paradigm and post-processing techniques were implemented. Overall, the 3-encoder model achieved the best performance in terms of Dice Score on TC and WT when trained with Dice Loss and on ET when trained with Generalized Dice Focal Loss. SegResNet showed higher recall on TC and WT, and higher precision on ET. After post-processing, we reached Dice Scores of 0.843, 0.869, 0.757 with the pre-trained model and 0.852, 0.876, 0.764 with the ensemble model for TC, WT and ET, respectively. Both strategies yielded state-of-the-art performances, although the ensemble demonstrated significantly superior results. Segmentation of the ET region was improved after post-processing, which increased test metrics while maintaining the integrity of the data. |
| format | Article |
| id | doaj-art-a8028cbff08f46ca909b5938df5b829a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-a8028cbff08f46ca909b5938df5b829a2025-08-20T04:01:34ZengNature PortfolioScientific Reports2045-23222025-07-0115111510.1038/s41598-025-07257-2Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRIAnnachiara Cariola0Elena Sibilano1Andrea Guerriero2Vitoantonio Bevilacqua3Antonio Brunetti4Department of Electrical and Information Engineering, Polytechnic University of BariDepartment of Electrical and Information Engineering, Polytechnic University of BariDepartment of Electrical and Information Engineering, Polytechnic University of BariDepartment of Electrical and Information Engineering, Polytechnic University of BariDepartment of Electrical and Information Engineering, Polytechnic University of BariAbstract Automated segmentation of pediatric brain tumors (PBTs) can support precise diagnosis and treatment monitoring, but it is still poorly investigated in literature. This study proposes two different Deep Learning approaches for semantic segmentation of tumor regions in PBTs from MRI scans. Two pipelines were developed for segmenting enhanced tumor (ET), tumor core (TC), and whole tumor (WT) in pediatric gliomas from the BraTS-PEDs 2024 dataset. First, a pre-trained SegResNet model was retrained with a transfer learning approach and tested on the pediatric cohort. Then, two novel multi-encoder architectures leveraging the attention mechanism were designed and trained from scratch. To enhance the performance on ET regions, an ensemble paradigm and post-processing techniques were implemented. Overall, the 3-encoder model achieved the best performance in terms of Dice Score on TC and WT when trained with Dice Loss and on ET when trained with Generalized Dice Focal Loss. SegResNet showed higher recall on TC and WT, and higher precision on ET. After post-processing, we reached Dice Scores of 0.843, 0.869, 0.757 with the pre-trained model and 0.852, 0.876, 0.764 with the ensemble model for TC, WT and ET, respectively. Both strategies yielded state-of-the-art performances, although the ensemble demonstrated significantly superior results. Segmentation of the ET region was improved after post-processing, which increased test metrics while maintaining the integrity of the data.https://doi.org/10.1038/s41598-025-07257-2Pediatric brain tumorDeep learningTumor segmentationMRI |
| spellingShingle | Annachiara Cariola Elena Sibilano Andrea Guerriero Vitoantonio Bevilacqua Antonio Brunetti Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI Scientific Reports Pediatric brain tumor Deep learning Tumor segmentation MRI |
| title | Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI |
| title_full | Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI |
| title_fullStr | Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI |
| title_full_unstemmed | Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI |
| title_short | Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI |
| title_sort | deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric mri |
| topic | Pediatric brain tumor Deep learning Tumor segmentation MRI |
| url | https://doi.org/10.1038/s41598-025-07257-2 |
| work_keys_str_mv | AT annachiaracariola deeplearningstrategiesforsemanticsegmentationofpediatricbraintumorsinmultiparametricmri AT elenasibilano deeplearningstrategiesforsemanticsegmentationofpediatricbraintumorsinmultiparametricmri AT andreaguerriero deeplearningstrategiesforsemanticsegmentationofpediatricbraintumorsinmultiparametricmri AT vitoantoniobevilacqua deeplearningstrategiesforsemanticsegmentationofpediatricbraintumorsinmultiparametricmri AT antoniobrunetti deeplearningstrategiesforsemanticsegmentationofpediatricbraintumorsinmultiparametricmri |