Characterization of Small Nodules by Automatic Segmentation of X-ray Computed Tomography Images

Peng Tao, Friederike Griess, Yelena Lvov, Mikhail Mineyev, Binsheng Zhao, David Levin, Leon Kaufman

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Objective: To characterize the ability of an automatic lung nodule segmentation algorithm to measure small nodule dimensions and growth rates. Methods: A phantom of 20 sets of 6 balls each (11 different nylon balls and 9 acrylic balls) of 1 to 9.5 mm in diameter, in foam, was imaged using x-ray computed tomography with slice thicknesses of 5, 2.5, and 1.25 mm, pitches of 3 and 6, and standard and lung resolution. Measurements consisted of volume and maximum in-plane cross-sectional areas and their derived maximum and effective diameters. Growth rates were simulated using pairs of groups of balls. Results: Volume measurements overestimate volume, more so for thicker slices. For the largest balls, the error is 60% for 5-mm slices and 20% for 1.25-mm slices. Effective diameter calculated from volume better approximates actual diameter. For area measurements, errors are 0% to 5% for the largest balls, and the effective and actual diameters are closely matched. Conclusions: Below 5 mm in diameter, changes in volume should reach 100% for reliable indication of growth. Above 6 mm, the threshold for detecting change is on the order of 25% growth. Even under ideal conditions, results indicate the need for caution when making a diagnosis of malignancy on the basis of volume change.

Original languageEnglish (US)
Pages (from-to)372-377
Number of pages6
JournalJournal of computer assisted tomography
Volume28
Issue number3
DOIs
StatePublished - May 1 2004

Keywords

  • Automatic segmentation
  • Lung nodule

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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