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Proceedings Paper

Inter-algorithm lesion volumetry comparison of real and 3D simulated lung lesions in CT
Author(s): Marthony Robins; Justin Solomon; Jocelyn Hoye; Taylor Smith; Lukas Ebner; Ehsan Samei
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Paper Abstract

The purpose of this study was to establish volumetric exchangeability between real and computational lung lesions in CT. We compared the overall relative volume estimation performance of segmentation tools when used to measure real lesions in actual patient CT images and computational lesions virtually inserted into the same patient images (i.e., hybrid datasets). Pathologically confirmed malignancies from 30 thoracic patient cases from Reference Image Database to Evaluate Therapy Response (RIDER) were modeled and used as the basis for the comparison. Lesions included isolated nodules as well as those attached to the pleura or other lung structures. Patient images were acquired using a 16 detector row or 64 detector row CT scanner (Lightspeed 16 or VCT; GE Healthcare). Scans were acquired using standard chest protocols during a single breath-hold. Virtual 3D lesion models based on real lesions were developed in Duke Lesion Tool (Duke University), and inserted using a validated image-domain insertion program. Nodule volumes were estimated using multiple commercial segmentation tools (iNtuition, TeraRecon, Inc., Syngo.via, Siemens Healthcare, and IntelliSpace, Philips Healthcare). Consensus based volume comparison showed consistent trends in volume measurement between real and virtual lesions across all software. The average percent bias (± standard error) shows -9.2±3.2% for real lesions versus -6.7±1.2% for virtual lesions with tool A, 3.9±2.5% and 5.0±0.9% for tool B, and 5.3±2.3% and 1.8±0.8% for tool C, respectively. Virtual lesion volumes were statistically similar to those of real lesions (< 4% difference) with p >.05 in most cases. Results suggest that hybrid datasets had similar inter-algorithm variability compared to real datasets.

Paper Details

Date Published: 9 March 2017
PDF: 10 pages
Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101321S (9 March 2017); doi: 10.1117/12.2254219
Show Author Affiliations
Marthony Robins, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)
Duke Univ. Medical Ctr. (United States)
Justin Solomon, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)
Duke Univ. Medical Ctr. (United States)
Jocelyn Hoye, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)
Duke Univ. Medical Ctr. (United States)
Taylor Smith, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)
Duke Univ. Medical Ctr. (United States)
Lukas Ebner, Duke Univ. Medical Ctr. (United States)
Univ. of Bern (Switzerland)
Ehsan Samei, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)
Duke Univ. Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 10132:
Medical Imaging 2017: Physics of Medical Imaging
Thomas G. Flohr; Joseph Y. Lo; Taly Gilat Schmidt, Editor(s)

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