Name
Title | ||
|---|---|---|
Segmenting the Ischemic Penumbra: A Decision Forest Approach with Automatic Threshold Finding. |
Abstract | ||
|---|---|---|
We propose a fully automatic method for segmenting the ischemic penumbra, using image texture and spatial features and a modified Random Forest algorithm, which we call Segmentation Forests, which has been designed to adapt the original Random Forests algorithm of Breiman to the segmentation of medical images. The method was trained and tested on the SPES dataset, part of the ISLES MICCAI Grand Challenge. The method is fast, taking approximately six minutes to segment a new case, and yields convincing results. On the testing portion of the SPES dataset, the method achieved an average Dice coefficient of 0.82, with a standard deviation of 0.08. |
Year | Venue | Field |
|---|---|---|
2015 | Brainles@MICCAI | Penumbra,Market segmentation,Pattern recognition,Computer science,Segmentation,Sørensen–Dice coefficient,Image texture,Artificial intelligence,Random forest,Standard deviation |
DocType | Citations | PageRank |
Conference | 1 | 0.37 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Richard McKinley | 1 | 7 | 3.60 |
| Levin Häni | 2 | 1 | 0.37 |
| Roland Wiest | 3 | 344 | 22.73 |
| Mauricio Reyes | 4 | 73 | 13.74 |