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doi.org/10.1109/access.2019.2963254

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

..., Youchao Wang
1
IEEE Access3.90
Volume: 8, Pages: 8004 - 8018
Published: Jan 1, 2020
Abstract
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 time-consuming, operator intensive and subjective. Hence, this paper integrates the supervised learning and unsupervised learning to propose an...
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Paper Details
Title
DenseX-Net: An End-to-End Model for Lymphoma Segmentation in Whole-Body PET/CT Images
DOI
doi.org/10.1109/access.2019.2963254
Published Date
Jan 1, 2020
Journal
IEEE Access
Volume
8
Pages
8004 - 8018
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