Automated peripheral nerve segmentation for MR-neurography.
Eur Radiol Exp
; 8(1): 97, 2024 Aug 26.
Article
en En
| MEDLINE
| ID: mdl-39186183
ABSTRACT
BACKGROUND:
Magnetic resonance neurography (MRN) is increasingly used as a diagnostic tool for peripheral neuropathies. Quantitative measures enhance MRN interpretation but require nerve segmentation which is time-consuming and error-prone and has not become clinical routine. In this study, we applied neural networks for the automated segmentation of peripheral nerves.METHODS:
A neural segmentation network was trained to segment the sciatic nerve and its proximal branches on the MRN scans of the right and left upper leg of 35 healthy individuals, resulting in 70 training examples, via 5-fold cross-validation (CV). The model performance was evaluated on an independent test set of one-sided MRN scans of 60 healthy individuals.RESULTS:
Mean Dice similarity coefficient (DSC) in CV was 0.892 (95% confidence interval [CI] 0.888-0.897) with a mean Jaccard index (JI) of 0.806 (95% CI 0.799-0.814) and mean Hausdorff distance (HD) of 2.146 (95% CI 2.184-2.208). For the independent test set, DSC and JI were lower while HD was higher, with a mean DSC of 0.789 (95% CI 0.760-0.815), mean JI of 0.672 (95% CI 0.642-0.699), and mean HD of 2.118 (95% CI 2.047-2.190).CONCLUSION:
The deep learning-based segmentation model showed a good performance for the task of nerve segmentation. Future work will focus on extending training data and including individuals with peripheral neuropathies in training to enable advanced peripheral nerve disease characterization. RELEVANCE STATEMENT The results will serve as a baseline to build upon while developing an automated quantitative MRN feature analysis framework for application in routine reading of MRN examinations. KEY POINTS Quantitative measures enhance MRN interpretation, requiring complex and challenging nerve segmentation. We present a deep learning-based segmentation model with good performance. Our results may serve as a baseline for clinical automated quantitative MRN segmentation.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Imagen por Resonancia Magnética
Límite:
Adult
/
Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Eur Radiol Exp
Año:
2024
Tipo del documento:
Article
País de afiliación:
Alemania
Pais de publicación:
Reino Unido