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Proceedings Paper

Pulmonary nodule registration in serial CT scans using rib anatomy and nodule template matching
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Paper Abstract

The goal of this study was to develop an automated method to identify corresponding nodules in serial CT scans for interval change analysis. The method uses the rib centerlines as the reference for initial nodule registration. From an automatically-identified starting point near the spine, each rib is locally tracked and segmented by expectation-maximization. The ribs are automatically labeled, and the centerlines are estimated using skeletonization. 3D rigid affine transformation is used to register the individual ribs in the reference and target scans. For a given nodule in the reference scan, a search volume of interest (VOI) in the target scan is defined by using the registered ribs. Template matching guided by the normalized cross-correlation between the nodule template and target locations within the search VOI is used for refining the registration. The method was evaluated on 48 CT scans from 20 patients. The slice thickness ranged from 0.625 to 7 mm, and the in-plane pixel size from 0.556 to 0.82 mm. Experienced radiologists identified 101 pairs of nodules. Two metrics were used for performance evaluation: 1) the Euclidean distance between the nodule centers identified by the radiologist and the computer registration, and 2) a volume overlap measure defined as the intersection of the VOIs identified by the radiologist and the computer registration relative to the radiologist's VOI. The average Euclidean distance error was 2.7 ± 3.3 mm. Only 2 pairs had an error >10 mm. The average volume overlap measure was 0.71 ± 0.24. Eight-three out of 101 pairs had overlap ratios > 0.5 and only 2 pairs had no overlap.

Paper Details

Date Published: 5 April 2007
PDF: 9 pages
Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65140R (5 April 2007); doi: 10.1117/12.713409
Show Author Affiliations
Jiazheng Shi, The Univ. of Michigan (United States)
Berkman Sahiner, The Univ. of Michigan (United States)
Heang-Ping Chan, The Univ. of Michigan (United States)
Lubomir Hadjiiski, The Univ. of Michigan (United States)
Chuan Zhou, The Univ. of Michigan (United States)
Yi-Ta Wu, The Univ. of Michigan (United States)
Jun Wei, The Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 6514:
Medical Imaging 2007: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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