LNAS: A Clinically Applicable Deep-Learning System for Mediastinal Lymph Nodes Segmentation and Station Mapping Using Non-Contrast CT Images
Y Cao, J Feng, C Wang, F Yang, X Wang, X Jingxu… - papers.ssrn.com
Background: The accurate assessment of lymph node is an important part of the
management of lung cancer. Purpose: To assess the lymph nodes' segmentation, size, and
station by artificial intelligence (AI) for non-contrast chest CT images and evaluate its value
in clinical scenarios. Material and methods: This study proposed an end-to-end Lymph
Nodes Analysis System (LNAS) consisting of three models, which achieved automatic lymph
nodes segmentation, size measurement, and station mapping. The system was trained on …
management of lung cancer. Purpose: To assess the lymph nodes' segmentation, size, and
station by artificial intelligence (AI) for non-contrast chest CT images and evaluate its value
in clinical scenarios. Material and methods: This study proposed an end-to-end Lymph
Nodes Analysis System (LNAS) consisting of three models, which achieved automatic lymph
nodes segmentation, size measurement, and station mapping. The system was trained on …
LNAS: A clinically applicable deep-learning system for mediastinal enlarged lymph nodes segmentation and station mapping without regard to the pathogenesis using …
Y Cao, J Feng, C Wang, F Yang, X Wang, J Xu… - La radiologia …, 2024 - Springer
Background The accurate identification and evaluation of lymph nodes by CT images is of
great significance for disease diagnosis, treatment, and prognosis. Purpose To assess the
lymph nodes' segmentation, size, and station by artificial intelligence (AI) for unenhanced
chest CT images and evaluate its value in clinical scenarios. Material and methods This
retrospective study proposed an end-to-end Lymph Nodes Analysis System (LNAS)
consisting of three models: the Lymph Node Segmentation model (LNS), the Mediastinal …
great significance for disease diagnosis, treatment, and prognosis. Purpose To assess the
lymph nodes' segmentation, size, and station by artificial intelligence (AI) for unenhanced
chest CT images and evaluate its value in clinical scenarios. Material and methods This
retrospective study proposed an end-to-end Lymph Nodes Analysis System (LNAS)
consisting of three models: the Lymph Node Segmentation model (LNS), the Mediastinal …
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