Name
Title | ||
|---|---|---|
Fast And Uncertainty-Aware Cerebral Cortex Morphometry Estimation Using Random Forest Regression |
Abstract | ||
|---|---|---|
The cortical thickness and curvature of the human brain have proven to be valuable markers to detect and monitor neurodegenerative diseases [1]. Since the computational burden of currently available tools for brain morphometry is very high, this analysis often is only used for retrospective studies and not routinely in the clinics. A first attempt at a clinical use of cortical morphology is reported in [2]. We present an experiment for fast morphometry estimations using Random Forest (RF) regression [3] directly from MR imaging data. An uncertainty-aware voxel-wise, parcellation-wise, and multi-output model was built to estimate the thickness and mean curvature of the human cerebral cortex in 15 minutes instead of many hours for mesh-based tools. Preliminary results on a healthy controls database with 315 subjects show a substantial bias for the voxel-wise prediction, but high scan-rescan robustness, the proposed multi-output-parcellation prediction demonstrates the feasibility of the approach. |
Year | Venue | Keywords |
|---|---|---|
2018 | 2018 IEEE 15TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2018) | Human brain morphometry, cortical thickness, cortical curvature, random forest regression, machine learning |
Field | DocType | ISSN |
Mr imaging,Regression,Pattern recognition,Computer science,Robustness (computer science),Brain morphometry,Human brain,Artificial intelligence,Cerebral cortex,Random forest | Conference | 1945-7928 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yannick Suter | 1 | 1 | 0.69 |
| Christian Rummel | 2 | 6 | 2.27 |
| Roland Wiest | 3 | 344 | 22.73 |
| Mauricio Reyes | 4 | 73 | 13.74 |