Name
Abstract | ||
|---|---|---|
At liver surgery planning stage, the liver volumetry would be essential for surgeons. Main problem at liver extraction is the wide variability of livers in shapes and sizes. Since, hepatic blood vessels structure varies from a person to another and covers liver region, the present method uses that information for extraction of liver in two stages. The first stage is to extract abdominal blood vessels in the form of hepatic and nonhepatic blood vessels. At the second stage, extracted vessels are used to control extraction of liver region automatically. Contrast enhanced CT datasets at only the portal phase of 50 cases is used. Those data include 30 abnormal livers. A reference for all cases is done through a comparison of two experts labeling results and correction of their inter-reader variability. Results of the proposed method agree with the reference at an average rate of 97.8%. Through application of different metrics mentioned at MICCAI workshop for liver segmentation, it is found that: volume overlap error is 4.4%, volume difference is 0.3%, average symmetric distance is 0.7 mm, Root mean square symmetric distance is 0.8 mm, and maximum distance is 15.8 mm. These results represent the average of overall data and show an improved accuracy compared to current liver segmentation methods. It seems to be a promising method for extraction of liver volumetry of various shapes and sizes. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1117/12.911175 | Proceedings of SPIE |
Keywords | Field | DocType |
Liver extraction,portal phase CT dataset,blood vessel,volumetry | Nuclear medicine,Computer vision,Abdomen,Segmentation,Surgery planning,Root mean square,Artificial intelligence,Physics,Blood vessel | Conference |
Volume | ISSN | Citations |
8314 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| ahmed s maklad | 1 | 2 | 1.04 |
| mikio matsuhiro | 2 | 5 | 4.05 |
| hajime suzuki | 3 | 2 | 2.39 |
| Yoshiki Kawata | 4 | 192 | 54.44 |
| Noboru Niki | 5 | 188 | 66.10 |
| tohru utsunomiya | 6 | 2 | 0.70 |
| Mitsuo Shimada | 7 | 38 | 7.91 |