An Encoder-Decoder Neural Network With 3D Squeeze-and-Excitation and Deep Supervision for Brain Tumor Segmentation

Brain tumor segmentation from medical images is a prerequisite to provide a quantitative and intuitive reference for clinical diagnosis and treatment. Manual segmentation depends on clinicians' experience, and is laborious and time-consuming. To tackle these issues, we proposed an encoder-decod...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE access Ročník 8; s. 34029 - 34037
Hlavní autoři: Liu, Ping, Dou, Qi, Wang, Qiong, Heng, Pheng-Ann
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Brain
Brain cancer
Brain modeling
Brain tumor segmentation
Coders
Decoding
Encoders-Decoders
Excitation
Image segmentation
Magnetic resonance imaging
Medical imaging
Neural networks
squeeze-and-excitation
Three dimensional models
Three-dimensional displays
Training
Tumors
v-net
ISSN:2169-3536, 2169-3536
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Brain tumor segmentation from medical images is a prerequisite to provide a quantitative and intuitive reference for clinical diagnosis and treatment. Manual segmentation depends on clinicians' experience, and is laborious and time-consuming. To tackle these issues, we proposed an encoder-decoder neural network, i.e. deep supervised 3D Squeeze-and-Excitation V-Net (DSSE-V-Net) to segment brain tumors automatically. We modified V-Net by adding batch normalization and using bottom residual block to make the network deeper. Then we incorporated a squeeze & excitation(SE) module in the modified V-Net by adding the SE block in each stage of the encoder and decoder, respectively. We also integrated 3D deep supervision seamlessly into the network to accelerate convergence. We evaluated our model on the public BraTS 2017 dataset for brain tumor segmentation. Our model outperformed both 3D U-Net and modified V-Net, and obtained highly competitive performance compared with those methods winning in the BraTS 2017 challenge.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2973707