Brain and lesion segmentation in multiple sclerosis using fully convolutional neural networks: A large-scale study.
Mult Scler
; 26(10): 1217-1226, 2020 09.
Article
em En
| MEDLINE
| ID: mdl-31190607
ABSTRACT
OBJECTIVE:
To investigate the performance of deep learning (DL) based on fully convolutional neural network (FCNN) in segmenting brain tissues in a large cohort of multiple sclerosis (MS) patients.METHODS:
We developed a FCNN model to segment brain tissues, including T2-hyperintense MS lesions. The training, validation, and testing of FCNN were based on ~1000 magnetic resonance imaging (MRI) datasets acquired on relapsing-remitting MS patients, as a part of a phase 3 randomized clinical trial. Multimodal MRI data (dual-echo, FLAIR, and T1-weighted images) served as input to the network. Expert validated segmentation was used as the target for training the FCNN. We cross-validated our results using the leave-one-center-out approach.RESULTS:
We observed a high average (95% confidence limits) Dice similarity coefficient for all the segmented tissues 0.95 (0.92-0.98) for white matter, 0.96 (0.93-0.98) for gray matter, 0.99 (0.98-0.99) for cerebrospinal fluid, and 0.82 (0.63-1.0) for T2 lesions. High correlations between the DL segmented tissue volumes and ground truth were observed (R2 > 0.92 for all tissues). The cross validation showed consistent results across the centers for all tissues.CONCLUSION:
The results from this large-scale study suggest that deep FCNN can automatically segment MS brain tissues, including lesions, with high accuracy.Palavras-chave
Texto completo:
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Substância Branca
/
Esclerose Múltipla
Tipo de estudo:
Clinical_trials
Limite:
Humans
Idioma:
En
Revista:
Mult Scler
Assunto da revista:
NEUROLOGIA
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Estados Unidos