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Proceedings Paper

System for automatic detection of lung nodules exhibiting growth
Author(s): Carol L. Novak; Hong Shen; Benjamin L. Odry; Jane P. Ko; David P. Naidich
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Paper Abstract

Lung nodules that exhibit growth over time are considered highly suspicious for malignancy. We present a completely automated system for detection of growing lung nodules, using initial and follow-up multi-slice CT studies. The system begins with automatic detection of lung nodules in the later CT study, generating a preliminary list of candidate nodules. Next an automatic system for registering locations in two studies matches each candidate in the later study to its corresponding position in the earlier study. Then a method for automatic segmentation of lung nodules is applied to each candidate and its matching location, and the computed volumes are compared. The output of the system is a list of nodule candidates that are new or have exhibited volumetric growth since the previous scan. In a preliminary test of 10 patients examined by two radiologists, the automatic system identified 18 candidates as growing nodules. 7 (39%) of these corresponded to validated nodules or other focal abnormalities that exhibited growth. 4 of the 7 true detections had not been identified by either of the radiologists during their initial examinations of the studies. This technique represents a powerful method of surveillance that may reduce the probability of missing subtle or early malignant disease.

Paper Details

Date Published: 12 May 2004
PDF: 12 pages
Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.535389
Show Author Affiliations
Carol L. Novak, Siemens Corporate Research, Inc. (United States)
Hong Shen, Siemens Corporate Research, Inc. (United States)
Benjamin L. Odry, Siemens Corporate Research, Inc. (United States)
Jane P. Ko, New York Univ. Medical Ctr. (United States)
David P. Naidich, New York Univ. Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 5370:
Medical Imaging 2004: Image Processing
J. Michael Fitzpatrick; Milan Sonka, Editor(s)

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