Name
Title | ||
|---|---|---|
Automated linking of suspicious findings between automated 3D breast ultrasound volumes. |
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 ARC'S 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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1117/12.2214945 | Proceedings of SPIE |
Keywords | Field | DocType |
automated 3D breast ultrasound,computer-aided detection,personalized breast cancer screening,linking | Breast ultrasound,Computer vision,Ultrasonography,Stereoscopy,Computer aided detection,Artificial intelligence,Physics | Conference |
Volume | ISSN | Citations |
9785 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Albert Gubern-Mérida | 1 | 133 | 12.95 |
| Tao Tan | 2 | 46 | 10.25 |
| jan van zelst | 3 | 0 | 0.68 |
| ritse m mann | 4 | 104 | 9.39 |
| Nico Karssemeijer | 5 | 992 | 122.49 |