Name
Abstract | ||
|---|---|---|
Exploring the human structural connectome often involves dealing with millions of white matter tracts reconstructed from diffusion MRI. Reducing the dimensionality of such data by grouping tracts into bundles can prove essential for subsequent analyses. Many unsupervised clustering algorithms aim at providing such bundles but often require the choice of a distance metric and suffer from memory storage issues relating to the size of the datasets. We propose for the first time a neural network approach for the unsupervised clustering of white matter tracts. It has the main properties of learning automatically the tract features and scaling well with the data size. As a proof of concept, we compare both quantitatively and qualitatively the computed tract clusters with a commonly used clustering method. The proposed approach shows results similar to the reference approach while not using any distance matrix or similarity metric. |
Year | Venue | Keywords |
|---|---|---|
2018 | 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | Diffusion MRI, tractography, clustering, neural networks, restricted boltzmann machine |
Field | DocType | ISSN |
Diffusion MRI,Pattern recognition,Computer science,Connectome,Stochastic neural network,Metric (mathematics),Curse of dimensionality,Artificial intelligence,Distance matrix,Artificial neural network,Cluster analysis | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Dayan | 1 | 17 | 2.02 |
| Vasiliki-Maria Katsageorgiou | 2 | 4 | 2.74 |
| Luca Dodero | 3 | 16 | 3.55 |
| Vittorio Murino | 4 | 3277 | 207.20 |
| Diego Sona | 5 | 146 | 21.08 |