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Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans

: Kuhnigk, J.-M.; Dicken, V.; Bornemann, L.; Bakai, A.; Wormanns, D.; Krass, S.; Peitgen, H.-O.


IEEE transactions on medical imaging 25 (2006), No.4, pp.417-434
ISSN: 0278-0062
ISSN: 1558-254X
Journal Article
Fraunhofer MEVIS ()
analysis; CT; segmentation; volumetry; lung; lung cancer; cancer; screening; therapy; monitoring; method; algorithm; performance

Volumetric growth assessment of pulmonary lesions is crucial to both lung cancer screening and oncological therapy monitoring. While several methods for small pulmonary nodules have previously been presented, the segmentation of larger tumors that appear frequently in oncological patients and are more likely to be complexly interconnected with lung morphology has not yet received much attention. We present a fast, automated segmentation method that is based on morphological processing and is suitable for both small and large lesions. In addition, the proposed approach addresses clinical challenges to volume assessment such as variations in imaging protocol or inspiration state by introducing a method of segmentation-based partial volume analysis (SPVA) that follows on the segmentation procedure. Accuracy and reproducibility studies were performed to evaluate the new algorithms. In vivo interobserver and interscan studies on low-dose data from eight clinical metastasis patients revealed that clinically significant volume change can be detected reliably and with negligible computation time by the presented methods. In addition, phantom studies were conducted. Based on the segmentation performed with the proposed method, the performance of the SPVA volumetry method was compared with the conventional technique on a phantom that was scanned with different dosages and reconstructed with varying parameters. Both systematic and absolute errors were shown to be reduced substantially by the SPVA method. The method was especially successful in accounting for slice thickness and reconstruction kernel variations, where the median error was more than halved in comparison to the conventional approach.