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Proceedings Paper

Volume analysis of treatment response of head and neck lesions using 3D level set segmentation
Author(s): Lubomir Hadjiiski; Ethan Street; Berkman Sahiner; Sachin Gujar; Mohannad Ibrahim; Heang-Ping Chan; Suresh K. Mukherji
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Paper Abstract

A computerized system for segmenting lesions in head and neck CT scans was developed to assist radiologists in estimation of the response to treatment of malignant lesions. The system performs 3D segmentations based on a level set model and uses as input an approximate bounding box for the lesion of interest. In this preliminary study, CT scans from a pre-treatment exam and a post one-cycle chemotherapy exam of 13 patients containing head and neck neoplasms were used. A radiologist marked 35 temporal pairs of lesions. 13 pairs were primary site cancers and 22 pairs were metastatic lymph nodes. For all lesions, a radiologist outlined a contour on the best slice on both the pre- and post treatment scans. For the 13 primary lesion pairs, full 3D contours were also extracted by a radiologist. The average pre- and post-treatment areas on the best slices for all lesions were 4.5 and 2.1 cm2, respectively. For the 13 primary site pairs the average pre- and post-treatment primary lesions volumes were 15.4 and 6.7 cm3 respectively. The correlation between the automatic and manual estimates for the pre-to-post-treatment change in area for all 35 pairs was r=0.97, while the correlation for the percent change in area was r=0.80. The correlation for the change in volume for the 13 primary site pairs was r=0.89, while the correlation for the percent change in volume was r=0.79. The average signed percent error between the automatic and manual areas for all 70 lesions was 11.0±20.6%. The average signed percent error between the automatic and manual volumes for all 26 primary lesions was 37.8±42.1%. The preliminary results indicate that the automated segmentation system can reliably estimate tumor size change in response to treatment relative to radiologist's hand segmentation.

Paper Details

Date Published: 17 March 2008
PDF: 7 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 691512 (17 March 2008); doi: 10.1117/12.771314
Show Author Affiliations
Lubomir Hadjiiski, Univ. of Michigan (United States)
Ethan Street, Univ. of Michigan (United States)
Berkman Sahiner, Univ. of Michigan (United States)
Sachin Gujar, Univ. of Michigan (United States)
Mohannad Ibrahim, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Suresh K. Mukherji, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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