Patient-specific models for lung nodule detection and surveillance in CT images

Matthew S. Brown; Michael F. McNitt-Gray; Jonathan G. Goldin; Robert Suh; Denise R. Aberle

doi:10.1117/12.431146

3 July 2001 Patient-specific models for lung nodule detection and surveillance in CT images

Matthew S. Brown, Michael F. McNitt-Gray, Jonathan G. Goldin, Robert Suh, Denise R. Aberle

Proceedings Volume 4322, Medical Imaging 2001: Image Processing; (2001) https://doi.org/10.1117/12.431146
Event: Medical Imaging 2001, 2001, San Diego, CA, United States

Abstract

The purpose of this work is to automatically detect lung nodules in CT images, and then relocalize them in follow-up scans so that changes in size or morphology can be measured. We propose a new method that uses a patient's baseline image data to assist in the segmentation of subsequent images. The system uses a generic, a priori model to analyze the baseline scan of a previously unseen patient and then a user confirms or rejects nodule candidates. For analysis of follow-up scans of that particular patient, a patient- specific model is derived that narrows the search in feature-space for previously labeled nodules based on the feature values measured on the baseline scan. Also, some previously identified false positives can be automatically relocalized and eliminated. In the baseline scans of eleven patients, a radiologist identified a total of 14 nodules. All 14 nodules were detected automatically by the system with an average of 11 false positives per case. In follow- up scans, using patient-specified models, 12 of the 14 nodules were relocalized. There was one previously unseen nodule, that was detected by the system, with 9 false positives per follow-up case.

Citation Download Citation

Matthew S. Brown, Michael F. McNitt-Gray, Jonathan G. Goldin, Robert Suh, and Denise R. Aberle "Patient-specific models for lung nodule detection and surveillance in CT images", Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001); https://doi.org/10.1117/12.431146

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