Share Email Print
cover

Proceedings Paper

Automated linking of suspicious findings between automated 3D breast ultrasound volumes
Author(s): Albert Gubern-Mérida; Tao Tan; Jan van Zelst; Ritse M. Mann; Nico Karssemeijer
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Automated breast ultrasound (ABUS) is a 3D imaging technique which is rapidly emerging as a safe and relatively inexpensive modality for screening of women with dense breasts. However, reading ABUS examinations is very time consuming task since radiologists need to manually identify suspicious findings in all the different ABUS volumes available for each patient. Image analysis techniques to automatically link findings across volumes are required to speed up clinical workflow and make ABUS screening more efficient. In this study, we propose an automated system to, given the location in the ABUS volume being inspected (source), find the corresponding location in a target volume. The target volume can be a different view of the same study or the same view from a prior examination. The algorithm was evaluated using 118 linkages between suspicious abnormalities annotated in a dataset of ABUS images of 27 patients participating in a high risk screening program. The distance between the predicted location and the center of the annotated lesion in the target volume was computed for evaluation. The mean ± stdev and median distance error achieved by the presented algorithm for linkages between volumes of the same study was 7.75±6.71 mm and 5.16 mm, respectively. The performance was 9.54±7.87 and 8.00 mm (mean ± stdev and median) for linkages between volumes from current and prior examinations. The proposed approach has the potential to minimize user interaction for finding correspondences among ABUS volumes.

Paper Details

Date Published: 24 March 2016
PDF: 6 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97850N (24 March 2016); doi: 10.1117/12.2214945
Show Author Affiliations
Albert Gubern-Mérida, Radboud Univ. Medical Ctr. (Netherlands)
Tao Tan, Radboud Univ. Medical Ctr. (Netherlands)
Jan van Zelst, Radboud Univ. Medical Ctr. (Netherlands)
Ritse M. Mann, Radboud Univ. Medical Ctr. (Netherlands)
Nico Karssemeijer, Radboud Univ. Medical Ctr. (Netherlands)


Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato, Editor(s)

© SPIE. Terms of Use
Back to Top