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Proceedings Paper

Patient-specific models for lung nodule detection and surveillance in CT images
Author(s): Matthew S. Brown; Michael F. McNitt-Gray; Jonathan G. Goldin; Robert Suh; Denise R. Aberle
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Paper 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.

Paper Details

Date Published: 3 July 2001
PDF: 9 pages
Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001); doi: 10.1117/12.431146
Show Author Affiliations
Matthew S. Brown, Univ. of California/Los Angeles (United States)
Michael F. McNitt-Gray, Univ. of California/Los Angeles (United States)
Jonathan G. Goldin, Univ. of California/Los Angeles (United States)
Robert Suh, Univ. of California/Los Angeles (United States)
Denise R. Aberle, Univ. of California/Los Angeles (United States)


Published in SPIE Proceedings Vol. 4322:
Medical Imaging 2001: Image Processing
Milan Sonka; Kenneth M. Hanson, Editor(s)

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