Name
Title | ||
|---|---|---|
Exploring DeepMedic for the purpose of segmenting white matter hyperintensity lesions. |
Abstract | ||
|---|---|---|
DeepMedic, an open source software library based on a multi-channel multi-resolution 3D convolutional neural network, has recently been made publicly available for brain lesion segmentations. It has already been shown that segmentation tasks on MRI data of patients having traumatic brain injuries, brain tumors, and ischemic stroke lesions can be performed very well. In this paper we describe how it can efficiently be used for the purpose of detecting and segmenting white matter hyperintensity lesions. We examined if it can be applied to single-channel routine 2D FLAIR data. For evaluation, we annotated 197 datasets with different numbers and sizes of white matter hyperintensity lesions. Our experiments have shown that substantial results with respect to the segmentation quality can be achieved. Compared to the original parametrization of the DeepMedic neural network, the timings for training can be drastically reduced if adjusting corresponding training parameters, while at the same time the Dice coefficients remain nearly unchanged. This enables for performing a whole training process within a single day utilizing a NVIDIA GeForce GTX 580 graphics board which makes this library also very interesting for research purposes on low-end GPU hardware. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1117/12.2292809 | Proceedings of SPIE |
Keywords | DocType | Volume |
Machine learning,including deep learning,segmentation,applications: brain | Conference | 10575 |
ISSN | Citations | PageRank |
0277-786X | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fiona Lippert | 1 | 0 | 0.34 |
| Bastian Cheng | 2 | 0 | 0.34 |
| Amir Golsari | 3 | 0 | 0.34 |
| Florian Weiler | 4 | 23 | 4.01 |
| Johannes Gregori | 5 | 0 | 0.34 |
| Götz Thomalla | 6 | 2 | 1.32 |
| Jan Klein | 7 | 95 | 10.94 |