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Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network

Physics in Medicine and Biology

Dec 14, 2018

Xiangming ZhaoShan Tan

Abstract

Automatic tumor segmentation from medical images is an important step for computer-aided cancer diagnosis and treatment. Recently, deep learning has been successfully applied to this task, leading to state-of-the-art performance. However, most of existing deep learning segmentation meth...read more

Mentioned in this Paper

Study
Biological Neural Networks
4-phenyl-3-furoxancarbonitrile
Clinic
Positron-Emission Tomography
Three-dimensional
PET/CT scan
Neoplasms
Neural Stem Cells
APOBEC3D
3
40
Paper Details
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123
  • References23
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Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network

Physics in Medicine and Biology

Dec 14, 2018

Xiangming ZhaoShan Tan

PMID: 30523964

DOI: 10.1088/1361-6560/aaf44b

Abstract

Automatic tumor segmentation from medical images is an important step for computer-aided cancer diagnosis and treatment. Recently, deep learning has been successfully applied to this task, leading to state-of-the-art performance. However, most of existing deep learning segmentation meth...read more

Mentioned in this Paper

Study
Biological Neural Networks
4-phenyl-3-furoxancarbonitrile
Clinic
Positron-Emission Tomography
Three-dimensional
PET/CT scan
Neoplasms
Neural Stem Cells
APOBEC3D
3
40

Similar Papers Found In These Feeds

Neural Networks

Here is the latest research on physiological and in silico networks and circuits implicated in the nervous system.

Related Papers

IEEE Transactions on Medical Imaging

Optimal co-segmentation of tumor in PET-CT images with context information

IEEE Transactions on Medical ImagingMay 23, 2013
Qi SongXiaodong Wu
IEEE Transactions on Medical Imaging

Pelvic Organ Segmentation Using Distinctive Curve Guided Fully Convolutional Networks

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Paper Details
References
  • References23
  • Citations
123
  • References23
  • Citations
  • quote and clock

    No citations available

    This paper may not have been cited yet.
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