Enhanced three-dimensional U-Net with graph-based refining for segmentation of gastrointestinal stromal tumours

Enhanced three-dimensional U-Net with graph-based refining for segmentation of gastrointestinal stromal tumours

The gastrointestinal stromal tumour (GIST) is a common mesenchymal tumour that lacks specificity of clinical manifestations. Therefore, preoperative accurate localization and accurate prediction of tumour risk are of important clinical value. At present, the diagnosis of GIST relies mainly on manual...

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Bibliographic Details
Main Authors: Guan, Z. (Author), Li, Z. (Author), Wang, Q. (Author), Wu, H. (Author), Xie, F. (Author), Zhao, W. (Author)
Format: Article
Language:English
Published: John Wiley and Sons Inc 2021
Subjects:
biological organs
Biological organs
Clinical manifestation
computerised tomography
Computerized tomography
Convolution
Convolutional neural net
convolutional neural nets
Convolutional neural networks
Diagnosis
feature extraction
Features extraction
Gastrointestinal stromal tumors
graph theory
Graph theory
Graph-based
Graphic methods
image coding
Image enhancement
image fusion
Image fusion
image segmentation
Image segmentation
Images segmentations
medical image processing
Medical images processing
Medical imaging
Mesenchymal tumors
Small intestine
Tumor segmentation
Tumors
tumours
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