Computer Science > Computer Vision and Pattern Recognition
arXiv:2411.08537 (cs)
[Submitted on 13 Nov 2024]
Title:MLV$^2$-Net: Rater-Based Majority-Label Voting for Consistent Meningeal Lymphatic Vessel Segmentation
Authors:Fabian Bongratz,Markus Karmann,Adrian Holz,Moritz Bonhoeffer,Viktor Neumaier,Sarah Deli,Benita Schmitz-Koep,Claus Zimmer,Christian Sorg,Melissa Thalhammer,Dennis M Hedderich,Christian Wachinger
View a PDF of the paper titled MLV$^2$-Net: Rater-Based Majority-Label Voting for Consistent Meningeal Lymphatic Vessel Segmentation, by Fabian Bongratz and 11 other authors
View PDFHTML (experimental)Abstract:Meningeal lymphatic vessels (MLVs) are responsible for the drainage of waste products from the human brain. An impairment in their functionality has been associated with aging as well as brain disorders like multiple sclerosis and Alzheimer's disease. However, MLVs have only recently been described for the first time in magnetic resonance imaging (MRI), and their ramified structure renders manual segmentation particularly difficult. Further, as there is no consistent notion of their appearance, human-annotated MLV structures contain a high inter-rater variability that most automatic segmentation methods cannot take into account. In this work, we propose a new rater-aware training scheme for the popular nnU-Net model, and we explore rater-based ensembling strategies for accurate and consistent segmentation of MLVs. This enables us to boost nnU-Net's performance while obtaining explicit predictions in different annotation styles and a rater-based uncertainty estimation. Our final model, MLV$^2$-Net, achieves a Dice similarity coefficient of 0.806 with respect to the human reference standard. The model further matches the human inter-rater reliability and replicates age-related associations with MLV volume.
| Comments: | ML4H 2024 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2411.08537 [cs.CV] |
| (orarXiv:2411.08537v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2411.08537 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled MLV$^2$-Net: Rater-Based Majority-Label Voting for Consistent Meningeal Lymphatic Vessel Segmentation, by Fabian Bongratz and 11 other authors
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