DocumentCode :
881683
Title :
Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans
Author :
Kuhnigk, Jan-Martin ; Dicken, Volker ; Bornemann, Lars ; Bakai, Annemarie ; Wormanns, Dag ; Krass, Stefan ; Peitgen, Heinz-Otto
Author_Institution :
MeVisCenter for Med. Visualization & Diagnostic Syst., Univ.sallee, Bremen, Germany
Volume :
25
Issue :
4
fYear :
2006
fDate :
4/1/2006 12:00:00 AM
Firstpage :
417
Lastpage :
434
Abstract :
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.
Keywords :
cancer; computerised tomography; image reconstruction; image segmentation; lung; measurement errors; medical image processing; phantoms; tumours; absolute errors; clinical metastasis patients; image reconstruction; in vivo interobserver studies; in vivo interscan studies; lung cancer screening; median error; morphological segmentation; oncological therapy monitoring; partial volume analysis; phantom; solid pulmonary lesions; systematic errors; thoracic CT scans; tumor segmentation; volumetric growth assessment; volumetry; Cancer; Computed tomography; Image reconstruction; Image segmentation; Imaging phantoms; Lesions; Lungs; Medical treatment; Patient monitoring; Solids; Chest imaging; X-ray tomography; computer aided analysis; image segmentation; volume measurement; Algorithms; Artificial Intelligence; Coin Lesion, Pulmonary; Humans; Imaging, Three-Dimensional; Information Storage and Retrieval; Pattern Recognition, Automated; Phantoms, Imaging; Radiographic Image Enhancement; Radiographic Image Interpretation, Computer-Assisted; Radiography, Thoracic; Reproducibility of Results; Sensitivity and Specificity; Tomography, X-Ray Computed;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2006.871547
Filename :
1610747
Link To Document :
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