Congress:
ECR25
Poster Number:
C-14717
Type:
Poster: EPOS Radiologist (scientific)
Authorblock:
A. Olesinski, R. Lederman, J. Sosna, L. Joskowicz; Jerusalem/IL
Disclosures:
Alon Olesinski:
Consultant: HighRAD
Richard Lederman:
Consultant: HighRAD
Jacob Sosna:
Consultant: HighRAD
Leo Joskowicz:
Consultant: HighRAD and Ezra
Keywords:
Artificial Intelligence, Lymph nodes, Oncology, CT, CAD, Computer Applications-Detection, diagnosis, Cancer
Accurate assessment of mediastinal lymph nodes (LNs) in contrast-enhanced CT (ceCT) scans is essential for cancer staging and treatment planning. Current guidelines require measurement of enlarged lymph nodes whose short axis length (SAL) is >10mm. Manual detection and measurement of LNs is, however, time-consuming and subject to observer variability. We have developed a novel annotation-efficient semi-supervised deep learning method for automatic detection and segmentation of mediastinal lymph nodes in ceCT scans.
The aim of ths study is to evaluate the performance of a novel method for accurate detection and measurement of mediastinal lymph nodes in chest CT by annotation-efficient deep learning.