Name
Abstract | ||
|---|---|---|
Brain iron deposits have recently been suggested as biomarkers for small brain vessel diseases. Here, we present a novel, automated method for segmenting brain iron deposits in the basal ganglia from structural MRI data. It is based on minimum-variance clustering of intensities from T1and T2∗-weighted volumes, and a supervised cluster selection algorithm. This method was evaluated with MR data from 24 subjects and compared with iron deposit masks segmented manually by an experienced rater. A median Jaccard similarity index of 0.64 between manual and automatically generated segmentation masks is promising and encourages further investigations to improve the computing speed and accuracy of the method. |
Year | Venue | Field |
|---|---|---|
2011 | MIUA | Pattern recognition,Segmentation,Computer science,Selection algorithm,Artificial intelligence,Jaccard index,Cluster analysis,Basal ganglia,Small brain |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
3 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas Glatz | 1 | 9 | 2.47 |
| Maria C Valdés Hernández | 2 | 32 | 10.14 |
| Alexander J Kiker | 3 | 1 | 0.70 |
| Mark E Bastin | 4 | 102 | 10.88 |
| Susana Muñoz Maniega | 5 | 6 | 2.19 |
| Natalie A. Royle | 6 | 5 | 1.09 |
| Ian J Deary | 7 | 27 | 7.04 |
| Joanna M. Wardlaw | 8 | 108 | 17.52 |