Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation

Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation

Multimodal positron emission tomography-computed tomography (PET-CT) is used routinely in the assessment of cancer. PET-CT combines the high sensitivity for tumor detection of PET and anatomical information from CT. Tumor segmentation is a critical element of PET-CT but at present, the performance o...

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Bibliographic Details
Main Authors: Bi, L. (Author), Fu, X. (Author), Fulham, M. (Author), Kim, J. (Author), Kumar, A. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
Anatomical information
Article
artificial intelligence
attention
Attention
Automated segmentation method
Biological organs
Carcinoma, Non-Small-Cell Lung
Computed Tomography (PET-CT)
Computerized tomography
Convolutional Neural Network (CNN), Multimodal Image Segmentation, Positron Emission Tomography
Convolutional neural networks
Critical elements
deep learning
Deep learning
diagnostic imaging
Diseases
gadolinium
human
Humans
image analysis
image segmentation
Image segmentation
intensity modulated radiation therapy
learning
lung cancer
Lung Neoplasms
lung nodule
lung tumor
machine learning
major clinical study
mitochondrial biogenesis
nerve cell network
Neural Networks, Computer
non small cell lung cancer
Non small cell lung cancer
nuclear magnetic resonance imaging
positron emission tomography
Positron emission tomography
Positron Emission Tomography Computed Tomography
positron emission tomography-computed tomography
radiation dose
sensitivity and specificity
Similarity coefficients
soft tissue sarcoma
Soft tissue sarcoma
Spatial attention
stimulus
Tumor segmentation
Tumors
visual field
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