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Proceedings Paper

Automatic detection and segmentation of liver metastatic lesions on serial CT examinations
Author(s): Avi Ben Cohen; Idit Diamant; Eyal Klang; Michal Amitai; Hayit Greenspan
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Paper Abstract

In this paper we present a fully automated method for detection and segmentation of liver metastases on serial CT examinations (portal phase) given a 2D baseline segmentation mask. Our database contains 27 CT scans, baselines and follow-ups, of 12 patients and includes 22 test cases. Our method is based on the information given in the baseline CT scan which contains the lesion's segmentation mask marked manually by a radiologist. We use the 2D baseline segmentation mask to identify the lesion location in the follow-up CT scan using non-rigid image registration. The baseline CT scan is also used to locate regions of tissues surrounding the lesion and to map them onto the follow-up CT scan, in order to reduce the search area on the follow-up CT scan. Adaptive region-growing and mean-shift segmentation are used to obtain the final lesion segmentation. The segmentation results are compared to those obtained by a human radiologist. Compared to the reference standard our method made a correct RECIST 1.1 assessment for 21 out of 22 test cases. The average Dice index was 0.83 ± 0.07, average Hausdorff distance was 7.85± 4.84 mm, average sensitivity was 0.87 ± 0.11 and positive predictive value was 0.81 ± 0.10. The segmentation performance and the RECIST assessment results look promising. We are pursuing the methodology further with expansion to 3D segmentation while increasing the dataset we are collecting from the CT abdomen unit at Sheba medical center.

Paper Details

Date Published: 24 March 2014
PDF: 8 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903519 (24 March 2014); doi: 10.1117/12.2043718
Show Author Affiliations
Avi Ben Cohen, Tel Aviv Univ. (Israel)
Idit Diamant, Tel Aviv Univ. (Israel)
Eyal Klang, Sheba Medical Ctr. (Israel)
Michal Amitai, Sheba Medical Ctr. (Israel)
Hayit Greenspan, Tel Aviv Univ. (Israel)

Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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