Automatic segmentation of gadolinium-enhancing lesions in multiple sclerosis using deep learning from clinical MRI.
Gadolinium-enhancing lesions reflect active disease and are critical for in-patient monitoring in multiple sclerosis (MS). In this work, we have developed the first fully automated method to segment and count the gadolinium-enhancing lesions from routine clinical MRI of MS patients. The proposed met...
Main Authors: | Sibaji Gaj, Daniel Ontaneda, Kunio Nakamura |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2021-01-01
|
Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0255939 |
Similar Items
-
Gadolinium-enhanced brain lesions in multiple sclerosis relapse
by: L. Martín-Aguilar, et al.
Published: (2022-09-01) -
Perfusion MRI in automatic classification of multiple sclerosis lesion subtypes
by: Ehsan Homayouny, et al.
Published: (2022-06-01) -
Cerebral lesions of multiple sclerosis: is gadolinium always irreplaceable in assessing lesion activity?
by: Constantina Andrada Treaba, et al.
Published: (2014-03-01) -
Editorial: Automatic methods for multiple sclerosis new lesions detection and segmentation
by: Olivier Commowick, et al.
Published: (2023-03-01) -
Evaluating the effect of multiple sclerosis lesions on automatic brain structure segmentation
by: Sandra González-Villà, et al.
Published: (2017-01-01)