MRI-based two-stage deep learning model for automatic detection and segmentation of brain metastases.
Eur Radiol
; 33(5): 3521-3531, 2023 May.
Article
em En
| MEDLINE
| ID: mdl-36695903
ABSTRACT
OBJECTIVES:
To develop and validate a two-stage deep learning model for automatic detection and segmentation of brain metastases (BMs) in MRI images.METHODS:
In this retrospective study, T1-weighted (T1) and T1-weighted contrast-enhanced (T1ce) MRI images of 649 patients who underwent radiotherapy from August 2019 to January 2022 were included. A total of 5163 metastases were manually annotated by neuroradiologists. A two-stage deep learning model was developed for automatic detection and segmentation of BMs, which consisted of a lightweight segmentation network for generating metastases proposals and a multi-scale classification network for false-positive suppression. Its performance was evaluated by sensitivity, precision, F1-score, dice, and relative volume difference (RVD).RESULTS:
Six hundred forty-nine patients were randomly divided into training (n = 295), validation (n = 99), and testing (n = 255) sets. The proposed two-stage model achieved a sensitivity of 90% (1463/1632) and a precision of 56% (1463/2629) on the testing set, outperforming one-stage methods based on a single-shot detector, 3D U-Net, and nnU-Net, whose sensitivities were 78% (1276/1632), 79% (1290/1632), and 87% (1426/1632), and the precisions were 40% (1276/3222), 51% (1290/2507), and 53% (1426/2688), respectively. Particularly for BMs smaller than 5 mm, the proposed model achieved a sensitivity of 66% (116/177), far superior to one-stage models (21% (37/177), 36% (64/177), and 53% (93/177)). Furthermore, it also achieved high segmentation performance with an average dice of 81% and an average RVD of 20%.CONCLUSION:
A two-stage deep learning model can detect and segment BMs with high sensitivity and low volume error. KEY POINTS ⢠A two-stage deep learning model based on triple-channel MRI images identified brain metastases with 90% sensitivity and 56% precision. ⢠For brain metastases smaller than 5 mm, the proposed two-stage model achieved 66% sensitivity and 22% precision. ⢠For segmentation of brain metastases, the proposed two-stage model achieved a dice of 81% and a relative volume difference (RVD) of 20%.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias Encefálicas
/
Aprendizado Profundo
Tipo de estudo:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Eur Radiol
Assunto da revista:
RADIOLOGIA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China