Automatic identification of IASLC-defined mediastinal lymph node stations on CT scans using multi-atlas organ segmentation

Joanne Hoffman; Jiamin Liu; Evrim Turkbey; Lauren Kim; Ronald M. Summers

doi:10.1117/12.2082190

20 March 2015 Automatic identification of IASLC-defined mediastinal lymph node stations on CT scans using multi-atlas organ segmentation

Joanne Hoffman, Jiamin Liu, Evrim Turkbey, Lauren Kim, Ronald M. Summers

Author Affiliations +

Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 94141R (2015) https://doi.org/10.1117/12.2082190
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Station-labeling of mediastinal lymph nodes is typically performed to identify the location of enlarged nodes for cancer staging. Stations are usually assigned in clinical radiology practice manually by qualitative visual assessment on CT scans, which is time consuming and highly variable. In this paper, we developed a method that automatically recognizes the lymph node stations in thoracic CT scans based on the anatomical organs in the mediastinum. First, the trachea, lungs, and spines are automatically segmented to locate the mediastinum region. Then, eight more anatomical organs are simultaneously identified by multi-atlas segmentation. Finally, with the segmentation of those anatomical organs, we convert the text definitions of the International Association for the Study of Lung Cancer (IASLC) lymph node map into patient-specific color-coded CT image maps. Thus, a lymph node station is automatically assigned to each lymph node. We applied this system to CT scans of 86 patients with 336 mediastinal lymph nodes measuring equal or greater than 10 mm. 84.8% of mediastinal lymph nodes were correctly mapped to their stations.

Citation Download Citation

Joanne Hoffman, Jiamin Liu, Evrim Turkbey, Lauren Kim, and Ronald M. Summers "Automatic identification of IASLC-defined mediastinal lymph node stations on CT scans using multi-atlas organ segmentation", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94141R (20 March 2015); https://doi.org/10.1117/12.2082190

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available