Research on glioma magnetic resonance imaging segmentation based on dual-channel three-dimensional densely connected network / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 763-768, 2019.
Article
Dans Chinois
| WPRIM
| ID: wpr-774144
ABSTRACT
Focus on the inconsistency of the shape, location and size of brain glioma, a dual-channel 3-dimensional (3D) densely connected network is proposed to automatically segment brain glioma tumor on magnetic resonance images. Our method is based on a 3D convolutional neural network frame, and two convolution kernel sizes are adopted in each channel to extract multi-scale features in different scales of receptive fields. Then we construct two densely connected blocks in each pathway for feature learning and transmission. Finally, the concatenation of two pathway features was sent to classification layer to classify central region voxels to segment brain tumor automatically. We train and test our model on open brain tumor segmentation challenge dataset, and we also compared our results with other models. Experimental results show that our algorithm can segment different tumor lesions more accurately. It has important application value in the clinical diagnosis and treatment of brain tumor diseases.
Texte intégral:
Disponible
Indice:
WPRIM (Pacifique occidental)
Sujet Principal:
Traitement d'image par ordinateur
/
Tumeurs du cerveau
/
Imagerie diagnostique
/
Imagerie par résonance magnétique
/
/
Gliome
Type d'étude:
Etude diagnostique
Limites du sujet:
Humains
langue:
Chinois
Texte intégral:
Journal of Biomedical Engineering
Année:
2019
Type:
Article
Documents relatifs à ce sujet
MEDLINE
...
LILACS
LIS