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2 results on '"Cuadra MB"'

1. Partial-volume modeling reveals reduced gray matter in specific thalamic nuclei early in the time course of psychosis and chronic schizophrenia

Author

Aleman-Gomez, Y, Najdenovska, E, Roine, T, Fartaria, MJ, Canales-Rodriguez, EJ, Rovo, Z, Hagmann, P, Conus, P, Do, KQ, Klauser, P, Steullet, P, Baumann, PS, Cuadra, MB, Aleman-Gomez, Y, Najdenovska, E, Roine, T, Fartaria, MJ, Canales-Rodriguez, EJ, Rovo, Z, Hagmann, P, Conus, P, Do, KQ, Klauser, P, Steullet, P, Baumann, PS, and Cuadra, MB

Abstract

The structural complexity of the thalamus, due to its mixed composition of gray and white matter, make it challenging to disjoint and quantify each tissue contribution to the thalamic anatomy. This work promotes the use of partial-volume-based over probabilistic-based tissue segmentation approaches to better capture thalamic gray matter differences between patients at different stages of psychosis (early and chronic) and healthy controls. The study was performed on a cohort of 23 patients with schizophrenia, 41 with early psychosis and 69 age and sex-matched healthy subjects. Six tissue segmentation approaches were employed to obtain the gray matter concentration/probability images. The statistical tests were applied at three different anatomical scales: whole thalamus, thalamic subregions and voxel-wise. The results suggest that the partial volume model estimation of gray matter is more sensitive to detect atrophies within the thalamus of patients with psychosis. However all the methods detected gray matter deficit in the pulvinar, particularly in early stages of psychosis. This study demonstrates also that the gray matter decrease varies nonlinearly with age and between nuclei. While a gray matter loss was found in the pulvinar of patients in both stages of psychosis, reduced gray matter in the mediodorsal was only observed in early psychosis subjects. Finally, our analyses point to alterations in a sub-region comprising the lateral posterior and ventral posterior nuclei. The obtained results reinforce the hypothesis that thalamic gray matter assessment is more reliable when the tissues segmentation method takes into account the partial volume effect.

Published
2020

2. CVSnet: A machine learning approach for automated central vein sign assessment in multiple sclerosis.

Author

Maggi P, Fartaria MJ, Jorge J, La Rosa F, Absinta M, Sati P, Meuli R, Du Pasquier R, Reich DS, Cuadra MB, Granziera C, Richiardi J, and Kober T

Subjects
Automation, Humans, Imaging, Three-Dimensional, Magnetic Resonance Imaging, White Matter diagnostic imaging, Machine Learning, Multiple Sclerosis diagnostic imaging, Software, Veins diagnostic imaging
Abstract

The central vein sign (CVS) is an efficient imaging biomarker for multiple sclerosis (MS) diagnosis, but its application in clinical routine is limited by inter-rater variability and the expenditure of time associated with manual assessment. We describe a deep learning-based prototype for automated assessment of the CVS in white matter MS lesions using data from three different imaging centers. We retrospectively analyzed data from 3 T magnetic resonance images acquired on four scanners from two different vendors, including adults with MS (n = 42), MS mimics (n = 33, encompassing 12 distinct neurological diseases mimicking MS) and uncertain diagnosis (n = 5). Brain white matter lesions were manually segmented on FLAIR* images. Perivenular assessment was performed according to consensus guidelines and used as ground truth, yielding 539 CVS-positive (CVS + ) and 448 CVS-negative (CVS - ) lesions. A 3D convolutional neural network ("CVSnet") was designed and trained on 47 datasets, keeping 33 for testing. FLAIR* lesion patches of CVS + /CVS - lesions were used for training and validation (n = 375/298) and for testing (n = 164/150). Performance was evaluated lesion-wise and subject-wise and compared with a state-of-the-art vesselness filtering approach through McNemar's test. The proposed CVSnet approached human performance, with lesion-wise median balanced accuracy of 81%, and subject-wise balanced accuracy of 89% on the validation set, and 91% on the test set. The process of CVS assessment, in previously manually segmented lesions, was ~ 600-fold faster using the proposed CVSnet compared with human visual assessment (test set: 4 seconds vs. 40 minutes). On the validation and test sets, the lesion-wise performance outperformed the vesselness filter method (P < 0.001). The proposed deep learning prototype shows promising performance in differentiating MS from its mimics. Our approach was evaluated using data from different hospitals, enabling larger multicenter trials to evaluate the benefit of introducing the CVS marker into MS diagnostic criteria., (© 2020 John Wiley & Sons, Ltd.)

Published
2020
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