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Brain tumor segmentation based on the U-NET+⁣+ network with efficientnet encoder.
Chen, Yunyi; Quan, Lan; Long, Chao; Chen, Yuxuan; Zu, Li; Huang, Chenxi.
Afiliación
  • Chen Y; Key Open Project of Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Haikou, Hainan, China.
  • Quan L; Department of Software Engineering, School of Informatics, Xiamen University, Xiamen, Fujian, China.
  • Long C; Key Open Project of Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Haikou, Hainan, China.
  • Chen Y; Department of Neurology and Department of Neuroscience, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China.
  • Zu L; Xiamen Key Laboratory of Brain Center, Xiamen, Fujian, China.
  • Huang C; Key Open Project of Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Haikou, Hainan, China.
Technol Health Care ; 32(S1): 183-195, 2024.
Article en En | MEDLINE | ID: mdl-38759048
ABSTRACT

BACKGROUND:

Brain tumor is a highly destructive, aggressive, and fatal disease. The presence of brain tumors can disrupt the brain's ability to control body movements, consciousness, sensations, thoughts, speech, and memory. Brain tumors are often accompanied by symptoms like epilepsy, headaches, and sensory loss, leading to varying degrees of cognitive impairment in affected patients.

OBJECTIVE:

The study goal is to develop an effective method to detect and segment brain tumor with high accurancy.

METHODS:

This paper proposes a novel U-Net+⁣+ network using EfficientNet as the encoder to segment brain tumors based on MRI images. We adjust the original U-Net+⁣+ model by removing the dense skip connections between sub-networks to simplify computational complexity and improve model efficiency, while the connections of feature maps at the same resolution level are retained to bridge the semantic gap.

RESULTS:

The proposed segmentation model is trained and tested on Kaggle's LGG brain tumor dataset, which obtains a satisfying performance with a Dice coefficient of 0.9180.

CONCLUSION:

This paper conducts research on brain tumor segmentation, using the U-Net+⁣+ network with EfficientNet as an encoder to segment brain tumors based on MRI images. We adjust the original U-Net+⁣+ model to simplify calculations and maintains rich semantic spatial features at the same time. Multiple loss functions are compared in this study and their effectiveness are discussed. The experimental results shows the model achieves a high segmention result with Dice coefficient of 0.9180.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Encefálicas / Imagen por Resonancia Magnética / Redes Neurales de la Computación Límite: Humans Idioma: En Revista: Technol Health Care Asunto de la revista: ENGENHARIA BIOMEDICA / SERVICOS DE SAUDE Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Encefálicas / Imagen por Resonancia Magnética / Redes Neurales de la Computación Límite: Humans Idioma: En Revista: Technol Health Care Asunto de la revista: ENGENHARIA BIOMEDICA / SERVICOS DE SAUDE Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Países Bajos