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

Jiazheng Shi; Berkman Sahiner; Heang-Ping Chan; Lubomir Hadjiiski; Chuan Zhou; Yi-Ta Wu; Jun Wei

doi:10.1117/12.713409

5 April 2007 Pulmonary nodule registration in serial CT scans using rib anatomy and nodule template matching

Jiazheng Shi, Berkman Sahiner, Heang-Ping Chan, Lubomir Hadjiiski, Chuan Zhou, Yi-Ta Wu, Jun Wei

Proceedings Volume 6514, Medical Imaging 2007: Computer-Aided Diagnosis; 65140R (2007) https://doi.org/10.1117/12.713409
Event: Medical Imaging, 2007, San Diego, CA, United States

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.

Citation Download Citation

Jiazheng Shi, Berkman Sahiner, Heang-Ping Chan, Lubomir Hadjiiski, Chuan Zhou, Yi-Ta Wu, and Jun Wei "Pulmonary nodule registration in serial CT scans using rib anatomy and nodule template matching", Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65140R (5 April 2007); https://doi.org/10.1117/12.713409

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KEYWORDS

Computed tomography

Lung

Image registration

3D acquisition

Image segmentation

Spine

Expectation maximization algorithms

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