DenseX-Net: An End-to-End Model for Lymphoma Segmentation in Whole-Body PET/CT Images

DenseX-Net: An End-to-End Model for Lymphoma Segmentation in Whole-Body PET/CT Images

Automatic lymphoma detection and accurate lymphoma boundary delineation from whole body Positron Emission Tomography/Computed Tomography (PET/CT) scans are essential for surgical navigation and radiation therapy. Besides, labeling the data, which means contouring the lymphoma contour in images is ti...

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Bibliographic Details
Main Authors: Haoming Li, Huiyan Jiang, Siqi Li, Meng Wang, Zhiguo Wang, Guoxiu Lu, Jia Guo, Youchao Wang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Lymphoma segmentation
deep learning
PET/CT
semi-supervised learning
computer aided diagnosis
Online Access:https://ieeexplore.ieee.org/document/8946601/

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