Name
Abstract | ||
|---|---|---|
For establishing a plan of Living Donor Liver Transplantation (LDLT), it is very important to estimate the volume of each liver segment. Usually Couinaud's classification is used to segment a liver, which is based on the liver anatomy. However, it is not easy to perform this method in a 3D space directly. In this paper, a fast segment method based on the hepatic vessel tree was proposed. This method was composed of four main steps: vasculature segmentation, 3D thinning, vascular tree pruning and classification, and vascular projection and curve fitting. This method was validated by application to a 3D liver from CT data, and it was shown to approximate closely Couinaud's classification with high speed. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-79490-5_33 | MIMI |
Keywords | Field | DocType |
fast method,vascular tree pruning,fast segment method,curve fitting,donor liver transplantation,high speed,liver anatomy,ct data,vascular projection,hepatic vessel tree,liver segment,volumetric analysis | Computer vision,Curve fitting,Computer science,Segmentation,Artificial intelligence,Liver segment,Liver transplantation | Conference |
Volume | ISSN | Citations |
4987 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 3 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaohui Huang | 1 | 7 | 2.25 |
| Boliang Wang | 2 | 26 | 4.61 |
| Ming Cheng | 3 | 54 | 13.93 |
| Wei-Li Wu | 4 | 0 | 0.34 |
| Xiao-Yang Huang | 5 | 7 | 1.58 |
| ying ai ju | 6 | 56 | 5.05 |