Postoperative glioma segmentation in CT image using deep feature fusion model guided by multi-sequence MRIs.
Eur Radiol
; 30(2): 823-832, 2020 Feb.
Article
em En
| MEDLINE
| ID: mdl-31650265
ABSTRACT
OBJECTIVES:
Computed tomography (CT) and magnetic resonance imaging (MRI) are the most commonly selected methods for imaging gliomas. Clinically, radiotherapists always delineate the CT glioma region with reference to multi-modal MR image information. On this basis, we develop a deep feature fusion model (DFFM) guided by multi-sequence MRIs for postoperative glioma segmentation in CT images.METHODS:
DFFM is a multi-sequence MRI-guided convolutional neural network (CNN) that iteratively learns the deep features from CT images and multi-sequence MR images simultaneously by utilizing a multi-channel CNN architecture, and then combines these two deep features together to produce the segmentation result. The whole network is optimized together via a standard back-propagation. A total of 59 CT and MRI datasets (T1/T2-weighted FLAIR, T1-weighted contrast-enhanced, T2-weighted) of postoperative gliomas as tumor grade II (n = 24), grade III (n = 18), or grade IV (n = 17) were included. Dice coefficient (DSC), precision, and recall were used to measure the overlap between automated segmentation results and manual segmentation. The Wilcoxon signed-rank test was used for statistical analysis.RESULTS:
DFFM showed a significantly (p < 0.01) higher DSC of 0.836 than U-Net trained by single CT images and U-Net trained by stacking the CT and multi-sequence MR images, which yielded 0.713 DSC and 0.818 DSC, respectively. The precision values showed similar behavior as DSC. Moreover, DSC and precision values have no significant statistical difference (p > 0.01) with difference grades.CONCLUSIONS:
DFFM enables the accurate automated segmentation of CT postoperative gliomas of profit guided by multi-sequence MR images and may thus improve and facilitate radiotherapy planning. KEY POINTS ⢠A fully automated deep learning method was developed to segment postoperative gliomas on CT images guided by multi-sequence MRIs. ⢠CT and multi-sequence MR image integration allows for improvements in deep learning postoperative glioma segmentation method. ⢠This deep feature fusion model produces reliable segmentation results and could be useful in delineating GTV in postoperative glioma radiotherapy planning.Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Glioma
Tipo de estudo:
Guideline
/
Observational_studies
/
Prognostic_studies
Limite:
Adolescent
/
Adult
/
Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
Revista:
Eur Radiol
Assunto da revista:
RADIOLOGIA
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
China