405-02-007 ISMRM Abstract

Sex-Related Differences in White Matter Hyperintensity Segmentation: Insights from Six Automated Methods

Primary: Analysis Methods - Segmentation and Detection

Secondary: Neuro - Multiple Sclerosis

405-02-007 · What's New in Multiple Sclerosis? · Tuesday, 12 May, 8:20 AM–10:10 AM · Ballroom West

Keywords: MRI Automated segmentation Sex differences White Matter Hyperintensities Segmentation Segmentation Quality

Accepted

Joany Rodrigues ¹, Mariana Bento^2,3,4, Leticia Rittner¹

¹School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil

²Biomedical Engineering, University of Calgary, Calgary, Canada

³Electrical and Software Engineering, University of Calgary, Calgary, Canada

⁴Alberta Children's Hospital Research Insitute, Calgary, Canada

Presenting Author: Joany Rodrigues

Synopsis

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References

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2. Balakrishnan, Ramya, Maria del C. Valdés Hernández, and Andrew J. Farrall. "Automatic segmentation of white matter hyperintensities from brain magnetic resonance images in the era of deep learning and big data–a systematic review." Computerized Medical Imaging and Graphics 88 (2021): 101867. doi: 10.1016/j.compmedimag.2021.101867 [doi]

3. Ioannou, Stefanos, et al. "A study of demographic bias in CNN-based brain MR segmentation." International Workshop on machine learning in clinical neuroimaging. Cham: Springer Nature Switzerland, 2022. doi: 10.1007/978-3-031-17899-3_2 [doi]

4. Lesjak, Žiga, et al. "A novel public MR image dataset of multiple sclerosis patients with lesion segmentations based on multi-rater consensus." Neuroinformatics 16.1 (2018): 51-63. doi: 10.1007/s12021-017-9348-7 [doi]

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6. Sundaresan, Vaanathi, et al. "Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images." Medical image analysis 73 (2021): 102184. doi: 10.1016/j.media.2021.102184 [doi]

7. Puonti, Oula, Juan Eugenio Iglesias, and Koen Van Leemput. "Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling." NeuroImage 143 (2016): 235-249. doi: 10.1016/j.neuroimage.2016.09.011 [doi]

8. Cerri, Stefano, et al. "A contrast-adaptive method for simultaneous whole-brain and lesion segmentation in multiple sclerosis." Neuroimage 225 (2021): 117471. doi: 10.1016/j.neuroimage.2020.117471 [doi]

9. Laso, P. "Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI.(2023) " arXiv preprint ARXIV.2312.05119. doi: 10.48550

10. Tsuchida, Ami, et al. "Early detection of white matter hyperintensities using SHIVA‐WMH detector." Human Brain Mapping 45.1 (2024): e26548. doi: 10.1002/hbm.26548 [doi]

11. Wiltgen, Tun, et al. "LST-AI: A deep learning ensemble for accurate MS lesion segmentation." NeuroImage: Clinical 42 (2024): 103611. doi: 10.1016/j.nicl.2024.103611 [doi]

12. Dice, Lee R. "Measures of the amount of ecologic association between species." Ecology 26.3 (1945): 297-302. doi: 10.2307/1932409 [doi]

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http://echo.ismrm.org/p/ISMRM2026/405-02-007