NOVEL STRUCTURAL-SCALE UNCERTAINTY MEASURES AND ERROR RETENTION CURVES: APPLICATION TO MULTIPLE SCLEROSIS
| Art der Publikation: | Artikel |
| Zitat: | |
| Zeitschrift: | Proceedings of International Symposium of Biomedical Imaging 2023 |
| Jahr: | 2023 |
| Abriss: | This paper focuses on the uncertainty estimation for white matter lesions (WML) segmentation in magnetic resonance imaging (MRI). On one side, voxel-scale segmentation er- rors cause the erroneous delineation of the lesions; on the other side, lesion-scale detection errors lead to wrong lesion counts. Both of these factors are clinically relevant for the assessment of multiple sclerosis patients. This work aims to compare the ability of different voxel- and lesion-scale un- certainty measures to capture errors related to segmentation and lesion detection, respectively. Our main contributions are (i) proposing new measures of lesion-scale uncertainty that do not utilise voxel-scale uncertainties; (ii) extending an error retention curves analysis framework for the eval- uation of lesion-scale uncertainty measures. Our results obtained on the multi-center testing set of 58 patients demon- strate that the proposed lesion-scale measure achieves the best performance among the analysed measures. All code implementations are provided at https://github.com/ Medical-Image-Analysis-Laboratory/MS_WML_ uncs. |
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| Hinzugefügt von: | [] |
| Gesamtbewertung: | 0 |
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