Brain Segmentation in Rodent MR-Images Using Convolutional Neural Networks
Research output: Contribution to conference › Conference abstract for conference › Research › peer-review
This study compares two different methods for the task of brain segmentation in rodent MR-images, a convolutional neural network (CNN) and majority voting of a registration based atlas (RBA) , and how limited training data affect their performance. The CNN was implemented in Tensorflow.
The RBA performs better on average when using a training set with fewer than 20 images but the CNN achieves a higher median dice-score with a training set of 19 images.
The RBA performs better on average when using a training set with fewer than 20 images but the CNN achieves a higher median dice-score with a training set of 19 images.
| Original language | English |
|---|---|
| Publication date | 2018 |
| Publication status | Published - 2018 |
| Event | Joint Annual Meeting ISMRM-ESMRMB 2018 - Paris, France Duration: 16 Jun 2018 → 21 Oct 2018 |
Conference
| Conference | Joint Annual Meeting ISMRM-ESMRMB 2018 |
|---|---|
| Country | France |
| City | Paris |
| Period | 16/06/2018 → 21/10/2018 |
ID: 204115825