Deep Learning for Localization of White Matter Lesions in Neurological Diseases

Julia Machnio, Mads Nielsen, Mostafa Mehdipour Ghazi
Proceedings of the 6th Northern Lights Deep Learning Conference (NLDL), PMLR 265:155-167, 2025.

Abstract

White Matter (WM) lesions, commonly observed as hyperintensities on FLAIR MRIs or hypointensities on T1-weighted images, are associated with neurological diseases. The spatial distribution of these lesions is linked to an increased risk of developing neurological conditions, emphasizing the need for location-based analyses. Traditional manual identification and localization of WM lesions are labor-intensive and time-consuming, highlighting the need for automated solutions. In this study, we propose novel deep learning-based methods for automated WM lesion segmentation and localization. Our approach utilizes state-of-the-art models to concurrently segment WM lesions and anatomical WM regions, providing detailed insights into their distribution within the brain’s anatomical structure. By applying k-means clustering to the regional WM lesion load, distinct subject groups are identified to be associated with various neurological conditions, validating the method’s alignment with established clinical findings. The robustness and adaptability of our method across different scanner types and imaging protocols make it a valuable tool for research and clinical practice, offering potential improvements in diagnostic efficiency and patient care.

Cite this Paper

BibTeX
@InProceedings{pmlr-v265-machnio25a, title = {Deep Learning for Localization of White Matter Lesions in Neurological Diseases}, author = {Machnio, Julia and Nielsen, Mads and Ghazi, Mostafa Mehdipour}, booktitle = {Proceedings of the 6th Northern Lights Deep Learning Conference (NLDL)}, pages = {155--167}, year = {2025}, editor = {Lutchyn, Tetiana and Ramírez Rivera, Adín and Ricaud, Benjamin}, volume = {265}, series = {Proceedings of Machine Learning Research}, month = {07--09 Jan}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v265/main/assets/machnio25a/machnio25a.pdf}, url = {https://proceedings.mlr.press/v265/machnio25a.html}, abstract = {White Matter (WM) lesions, commonly observed as hyperintensities on FLAIR MRIs or hypointensities on T1-weighted images, are associated with neurological diseases. The spatial distribution of these lesions is linked to an increased risk of developing neurological conditions, emphasizing the need for location-based analyses. Traditional manual identification and localization of WM lesions are labor-intensive and time-consuming, highlighting the need for automated solutions. In this study, we propose novel deep learning-based methods for automated WM lesion segmentation and localization. Our approach utilizes state-of-the-art models to concurrently segment WM lesions and anatomical WM regions, providing detailed insights into their distribution within the brain’s anatomical structure. By applying k-means clustering to the regional WM lesion load, distinct subject groups are identified to be associated with various neurological conditions, validating the method’s alignment with established clinical findings. The robustness and adaptability of our method across different scanner types and imaging protocols make it a valuable tool for research and clinical practice, offering potential improvements in diagnostic efficiency and patient care.} }
Endnote
%0 Conference Paper %T Deep Learning for Localization of White Matter Lesions in Neurological Diseases %A Julia Machnio %A Mads Nielsen %A Mostafa Mehdipour Ghazi %B Proceedings of the 6th Northern Lights Deep Learning Conference (NLDL) %C Proceedings of Machine Learning Research %D 2025 %E Tetiana Lutchyn %E Adín Ramírez Rivera %E Benjamin Ricaud %F pmlr-v265-machnio25a %I PMLR %P 155--167 %U https://proceedings.mlr.press/v265/machnio25a.html %V 265 %X White Matter (WM) lesions, commonly observed as hyperintensities on FLAIR MRIs or hypointensities on T1-weighted images, are associated with neurological diseases. The spatial distribution of these lesions is linked to an increased risk of developing neurological conditions, emphasizing the need for location-based analyses. Traditional manual identification and localization of WM lesions are labor-intensive and time-consuming, highlighting the need for automated solutions. In this study, we propose novel deep learning-based methods for automated WM lesion segmentation and localization. Our approach utilizes state-of-the-art models to concurrently segment WM lesions and anatomical WM regions, providing detailed insights into their distribution within the brain’s anatomical structure. By applying k-means clustering to the regional WM lesion load, distinct subject groups are identified to be associated with various neurological conditions, validating the method’s alignment with established clinical findings. The robustness and adaptability of our method across different scanner types and imaging protocols make it a valuable tool for research and clinical practice, offering potential improvements in diagnostic efficiency and patient care.
APA
Machnio, J., Nielsen, M. & Ghazi, M.M.. (2025). Deep Learning for Localization of White Matter Lesions in Neurological Diseases. Proceedings of the 6th Northern Lights Deep Learning Conference (NLDL), in Proceedings of Machine Learning Research 265:155-167 Available from https://proceedings.mlr.press/v265/machnio25a.html.

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