Toward Generalizable Multiple Sclerosis Lesion Segmentation Models
Automating Multiple Sclerosis (MS) lesion segmentation would be of great benefit in initial diagnosis as well as monitoring disease progression. Deep learning based segmentation models perform well in many domains, but the state-of-the-art in MS lesion segmentation is still suboptimal. Complementary...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11021622/ |
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| author | Liviu Badea Maria Popa |
| author_facet | Liviu Badea Maria Popa |
| author_sort | Liviu Badea |
| collection | DOAJ |
| description | Automating Multiple Sclerosis (MS) lesion segmentation would be of great benefit in initial diagnosis as well as monitoring disease progression. Deep learning based segmentation models perform well in many domains, but the state-of-the-art in MS lesion segmentation is still suboptimal. Complementary to previous MS lesion segmentation challenges which focused on optimizing the performance on a single evaluation dataset, this study aims to develop models that generalize across diverse evaluation datasets, mirroring real-world clinical scenarios that involve varied scanners, settings, and patient cohorts. To this end, we used all high-quality publicly-available MS lesion segmentation datasets on which we systematically trained a state-of-the-art UNet++ architecture. The resulting models demonstrate consistent performance across the remaining test datasets (are generalizable), with larger and more heterogeneous datasets leading to better models. To the best of our knowledge, this represents the most comprehensive cross-dataset evaluation of MS lesion segmentation models to date using publicly available datasets. Additionally, explicitly enhancing dataset size by merging datasets improved model performance. Specifically, a model trained on the combined MSSEG2016-train, ISBI2015, and 3D-MR-MS datasets surpasses the winner of the MICCAI-2016 competition. Moreover, we demonstrate that the generalizability of our models also relies on the use of quantile normalization on MRI intensities. |
| format | Article |
| id | doaj-art-f2080e3a7d7b4b3b9dc6f08ce16eed21 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f2080e3a7d7b4b3b9dc6f08ce16eed212025-08-20T03:10:07ZengIEEEIEEE Access2169-35362025-01-0113978599786910.1109/ACCESS.2025.357614511021622Toward Generalizable Multiple Sclerosis Lesion Segmentation ModelsLiviu Badea0https://orcid.org/0000-0002-1301-8985Maria Popa1https://orcid.org/0009-0009-9335-5954Artificial Intelligence Laboratory, National Institute for Research and Development in Informatics, Bucharest, RomaniaArtificial Intelligence Laboratory, National Institute for Research and Development in Informatics, Bucharest, RomaniaAutomating Multiple Sclerosis (MS) lesion segmentation would be of great benefit in initial diagnosis as well as monitoring disease progression. Deep learning based segmentation models perform well in many domains, but the state-of-the-art in MS lesion segmentation is still suboptimal. Complementary to previous MS lesion segmentation challenges which focused on optimizing the performance on a single evaluation dataset, this study aims to develop models that generalize across diverse evaluation datasets, mirroring real-world clinical scenarios that involve varied scanners, settings, and patient cohorts. To this end, we used all high-quality publicly-available MS lesion segmentation datasets on which we systematically trained a state-of-the-art UNet++ architecture. The resulting models demonstrate consistent performance across the remaining test datasets (are generalizable), with larger and more heterogeneous datasets leading to better models. To the best of our knowledge, this represents the most comprehensive cross-dataset evaluation of MS lesion segmentation models to date using publicly available datasets. Additionally, explicitly enhancing dataset size by merging datasets improved model performance. Specifically, a model trained on the combined MSSEG2016-train, ISBI2015, and 3D-MR-MS datasets surpasses the winner of the MICCAI-2016 competition. Moreover, we demonstrate that the generalizability of our models also relies on the use of quantile normalization on MRI intensities.https://ieeexplore.ieee.org/document/11021622/Multiple sclerosis (MS)brain MRIlesion segmentationdeep learningUNet++model generalizability |
| spellingShingle | Liviu Badea Maria Popa Toward Generalizable Multiple Sclerosis Lesion Segmentation Models IEEE Access Multiple sclerosis (MS) brain MRI lesion segmentation deep learning UNet++ model generalizability |
| title | Toward Generalizable Multiple Sclerosis Lesion Segmentation Models |
| title_full | Toward Generalizable Multiple Sclerosis Lesion Segmentation Models |
| title_fullStr | Toward Generalizable Multiple Sclerosis Lesion Segmentation Models |
| title_full_unstemmed | Toward Generalizable Multiple Sclerosis Lesion Segmentation Models |
| title_short | Toward Generalizable Multiple Sclerosis Lesion Segmentation Models |
| title_sort | toward generalizable multiple sclerosis lesion segmentation models |
| topic | Multiple sclerosis (MS) brain MRI lesion segmentation deep learning UNet++ model generalizability |
| url | https://ieeexplore.ieee.org/document/11021622/ |
| work_keys_str_mv | AT liviubadea towardgeneralizablemultiplesclerosislesionsegmentationmodels AT mariapopa towardgeneralizablemultiplesclerosislesionsegmentationmodels |