3D Segmentation of Necrotic Lung Lesions in CT Images Using Self-Supervised Contrastive Learning

3D Segmentation of Necrotic Lung Lesions in CT Images Using Self-Supervised Contrastive Learning

Deep convolutional neural networks (CNN) are often trained on 2D annotations created by radiologists following RECIST guidelines to segment lesions in 3D medical images. Three-dimensional segmentation is conducted by segmenting each lesion slice-by-slice on the axial direction and stacking the 2D se...

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Bibliographic Details
Main Authors: Yiqiao Liu, Sarah Halek, Randolph Crawford, Keith Persson, Michal Tomaszewski, Shubing Wang, Richard Baumgartner, Jianda Yuan, Gregory Goldmacher, Antong Chen
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Deep learning
necrotic lung lesion
lesion segmentation
self-supervised learning
semantic segmentation
Online Access:https://ieeexplore.ieee.org/document/10423778/

Internet

https://ieeexplore.ieee.org/document/10423778/

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